National Conference on Research Advances in Communication, Computation, Electrical Science and Structures (NCRACCESS-2015)
ISSN: 2348 - 8379 www.internationaljournalssrg.org Page 1
DETECTION OF AIR LEAKAGE IN BOILER RECTANGULAR
DUCT USING SIMULATION
P.Jayapriya
1 , P.Hari krishnan
2
1,2Department of electrical and electronics engineering,
Anna university regional centre, Coimbatore, India.
ABSTRACT: Leakage detection is one of
the most important design objectives in
large scale industries. Efficient and reliable
operation is important in boiler. Corrosion,
erosion and thermal stress can cause holes
and cracks resulting in a flow of water or
steam into the combustion side of the boiler.
The leakages can be located in either the
water or steam tubes. This project describes
the new approach to the detection of air
leakages in boiler rectangular duct by using
pressure sensor and its application in
practical use. The impacts of various factors
on the performance of the detection system
were discussed, including different sensing
elements. The automation was done by
using PIC microcontroller it would control
the levels of pressure. The simulation model
of the project was designed using proteus
software. The leakage occur in boiler duct,
it would predicated by pressure sensor and
LED. The results were obtained by using
LCD display.
Key words: Air leakage, boiler, rectangular
duct, pressure sensor.
I INTRODUCTION
A boiler or steam generator is a device
used to create steam by applying heat energy
to water. Any appliance that is constantly
exposed to water is prone to leaks, and steam
boilers are no exception. Care and
maintenance can go a long way toward
preventing leaks, but the corrosive effect of
water can only be held off for so long.
Leakage can occur at a number of points
inside and on the surface of steam boiler
machinery. The conventional method of
detecting tube leaks such as monitoring boiler
make up water, mass balancing or merely
depending on the human ear to recognise a
sound change, are not sufficiently sensitive for
large boilers. By using these methods the leak
is often big enough to have already caused
serious consequential damage sometimes to an
entire boiler face. Boiler tube leakage is a
major cause of outage and as consequence
power generation loss in thermal power plants
is huge. Leakage detection in recovery boilers
is important to avoid severe damage of
equipment .The walls of the furnace are
containing evaporating water with high
pressure.
Boiler tube leaks must be detected very
early, otherwise leaking steam may further
damage adjacent costly parts due to heavy
impact. Maintenance cost to secondary
damage from boiler tube leaks is very high
and repairing the damage requires several
weeks to complete. Due to the substantial
costs associated with any forced outage, it is
imperative to perform routine inspections. In
this manner, conditions with the potential to
result in failures can be identified, monitored
and addressed before they do result in failures.
The quantity of makeup water and hissing
sound emitted by leaking steam is detected by
human ears at the boiler. There are a number
of systems for detection of Tube leakage
available in both conventional boilers and
recovery boiler. The leakage of power
situation’s tubes like Pitot tube, water wall
tubes, super heater tubes, repeater tubes and
economizer tube, the detections are traditional
method.
The goal of this work can be briefed as
follows:
Design an efficient boiler
Early detection of leakage helps to
avoid damage
Improve boiler output.
National Conference on Research Advances in Communication, Computation, Electrical Science and Structures (NCRACCESS-2015)
ISSN: 2348 - 8379 www.internationaljournalssrg.org Page 2
.
II PRINCIPLE OF AIR LEAK TEST
The pressure used in the air leak test is
the atmospheric pressure. Atmospheric
pressure is the pressure generated by the
weight of air. As we live on earth,
atmospheric pressure is constantly applied
to us. The atmospheric pressure at sea level
is 101325Pa (1013hPa) which means that
we are pressed by a force of 1013hPa.The
level of atmospheric pressure changes
depending on the altitude. The
uncomfortable feeling we get on our
eardrum when we ride in an elevator to the
highest floor of a high-rise building is the
effect of the atmospheric pressure.
When the atmospheric pressure is
high, the amount of air per unit volume is
high and when the atmospheric pressure is
low, the amount of air per unit volume is
low. Air flows from where the atmospheric
pressure is high to where it is low and tries
to maintain a balanced condition of
atmospheric pressure. The air leak test is
performed using this transfer of air by
difference in pressure.
When air is charged to the work and
pressure inside the work increases, the
pressure difference between the inside and
outside of the work will be generated.
When the work has leak by a hole, the air
inside the work will flow outside and the
pressure inside the work will decrease. The
change of pressure inside the work is
monitored and a leak is detected through
the air leak test.
A NORMAL FACTOR OF.LEAKAGE
Types of leak openings include a
puncture, gash, or other corrosion hole,
very tiny pinhole leak crack or micro
crack, or in adequate sealing between
components or parts joined together. In
many cases, the location of a leak can be
determined by seeing material drip out at a
certain place. In some cases, it may known
or suspected there is a leak, but even the
location of the leak is not known. Since
leak openings are often so irregular, leaks
are sometimes sized by the leakage rate, as
in volume of fluid leaked per time, rather
than the size of the opening.
Leaks can occur or develop in many
different kinds of household, building,
vehicle, marine, aircraft, or industrial fluid
systems, whether the fluid is a gas or
liquid. Leaks in vehicle hydraulic systems
such as brake or power steering lines could
cause out leakage of brake or power
steering fluid resulting in failure of the
brakes, power steering, or other hydraulic
system. Also possible are leaks of engine
coolant - particularly in the radiator and at
the water pump seal, motor oil
and refrigerant in the conditioning system.
Some of these vehicle fluids have different
colours to help identify the type of leaking
fluid.
A system holding a full or
partial vacuum may have a leak causing in
leakage of air from the outside. Hazmat
procedures and/or teams may become
involved when leakage or spillage of
hazardous materials occurs. However, even
leakage of steam can be dangerous because
of the high temperature and energy of the
steam..
There can be numerous causes of
leaks. Leaks can occur from the outset
even during construction or initial
manufacture/assembly of fluid
systems. Pipes, tubing, valves, fittings, or
other components may be improperly
joined or welded together. Components
with threads may be improperly screwed
together .Often leaks are the result of
deterioration of materials from wear or
aging, such as rusting or other corrosion or
decomposition or
similar polymer materials used
as gaskets or other seals. For example,
wearing out of faucet washers causes water
to leak at the faucets. Cracks may result
from either outright damage, or wearing
out by stress such as fatigue failure or
corrosion such as cracking. Wearing out of
a surface between a disk and its seat in a
valve could cause a leak between ports
(valve inlets or outlets). Wearing out of
packing around a turning valve stem or
rotating centrifugal pump shaft could
develop into fluid leakage into the
environment. Similarly, wearing out of
seals or packing around piston-driven
pumps could also develop into out leakage
to the environment.
National Conference on Research Advances in Communication, Computation, Electrical Science and Structures (NCRACCESS-2015)
ISSN: 2348 - 8379 www.internationaljournalssrg.org Page 3
The pressure difference between both
sides of the leak can affect the movement
of material through the leak. Fluids will
commonly move from the higher pressure
side to the lower pressure side .The fluid
pressures on both sides include
the hydrostatic pressure, which is pressure
due to the weight from the height of fluid
level above the leak.
A smaller leak from a corrosion hole
can cause erosion on tubes close to the
leaking tube. The erosion can then lead to a
larger tube rupture with an extensive
leakage flow as result. The magnitude of a
leakage can be varying. Leakages have in
other models been simulated with flows
from 0.2 kg/s. Early-warning Leakage
detection by analysing the mass-balance on
the steam-side of the boiler is only possible
when a large leakage flow is present. This
is due to relatively low measurement
Precision on feed-water and steam-flow in
comparison to a leakage flow.
A leakage can also be detected on the
combustion side by indirect calculations of
mass-flows, but the precision is lower with
this method compared to the method
exploiting the mass-balance on the steam-
side. By combining the two balances,
indications on a leakage can be considered
from both the steam-side and the
combustion side.
B BOILER TUBE LEAK DETECTION
As a leak develops in a boiler tube,
turbulence by escaping fluid generates
pressure waves within the contained fluid
itself, throughout the flue gas into which
the fluid is escaping, and within the
container structure. These are commonly
referred to as airborne, and structure-borne
acoustic waves, respectively. To detect
leaks, the energy associated with these
mechanical waves are converted into
electrical signals with a variety of dynamic
pressure transducers (sensors) that are in
contact with the medium of interest.
Several methods of signal processing are
available that allow the voltages generated
by these sensors to be evaluated for the
presence of a leak. As mentioned above,
leaks in a boiler tube generate sound waves
in three media. The decision regarding
which types of acoustic waves are most
reliably detected is important from both
functional and economical considerations.
. Airborne methods are well
established and have detected leaks as
much as a week before any other means
available. In airborne applications,
microphones or low frequency resonant
piezoelectric transducers are coupled by
hollow waveguides to the gaseous furnace
medium. Most leak detection system
usually attaches waveguides through
penetrations in inspection doors, unused
soot-blower ports or the casing. The
structure-borne method of leak detection
has found applications in valves and
pressurized pipelines.
C.AIR LEAKAGE IN RECTANGULAR
DUCT
Duct leakage tester is a diagnostic tool
designed to measure the air tightness of
forced air heating, ventilating and air-
conditioning (HVAC) ductwork. A duct
leakage tester consists of a calibrated fan
for measuring an air flow rate and a
pressure sensing device to measure the
pressure created by the fan flow. The
combinations of pressure and fan flow
measurements are used to determine the
ductwork air tightness. The air tightness of
ductwork is useful knowledge when trying
to improve energy conservation.
A basic duct leakage testing
system includes three components: a
calibrated fan, a register sealing system,
and a device to measure fan flow and
building pressure. Supply registers or
return air grills are sealed using adhesive
tapes, cardboard, or non-adhesive reusable
seals .One register or return is left
unsealed, and the calibrated fan is
connected to it. Pressure is monitored in
one of the branches of the ductwork while
the calibrated fan delivers air into the
system. As air is delivered into the
ductwork, pressure builds and forces air
out of all of the openings in the various
ductwork connections or through the seams
and joints of the furnace or air-conditioner.
The tighter the ductwork system (e.g.
fewer holes), the less air is needed from the
fan to create a change in the ductwork
pressure.
National Conference on Research Advances in Communication, Computation, Electrical Science and Structures (NCRACCESS-2015)
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A duct leakage test can be performed
by either pressurizing or depressurizing the
ductwork. Ductwork that is outside the
building envelope, such as in an
unconditioned attic or crawlspace, should
be pressurized so as to not bring in
unwanted contaminants such as dust.
Figure1 Normal Rectangular duct
The volume of air which flows
through a closed window or door in a
given length of time as a result of the
difference in air pressure on its opposite
faces. In ductwork, air which escapes from
a joint, coupling, etc. The undesired
leakage or uncontrolled passage of air
from a ventilation system. The flow of
uncontrolled air through cracks or openings
in an enclosure within a building (such as a
HVAC plenum) or through the surfaces
which enclose the building.
D.BERNOULLI'S PRINCIPLE
Bernoulli's principle can be derived
from the principle of conservation of
energy. This states that, in a steady flow,
the sum of all forms of energy in a fluid
along a streamline is the same at all points
on that streamline. This requires that the
sum of kinetic energy, potential energy and
internal energy remains constant. Thus an
increase in the speed of the fluid –
implying an increase in both its dynamic
pressure and kinetic energy – occurs with a
simultaneous decrease in (the sum of) its
static pressure, potential energy and
internal energy. If the fluid is flowing out
of a reservoir, the sum of all forms of
energy is the same on all streamlines
because in a reservoir the energy per unit
volume (the sum of pressure and
gravitational potential ρ g h) is the same
everywhere.
Bernoulli's principle can also be
derived directly from Newton's 2nd law. If
a small volume of fluid is flowing
horizontally from a region of high pressure
to a region of low pressure, then there is
more pressure behind than in front. This
gives a net force on the volume,
accelerating it along the streamline.
Fluid particles are subject only to
pressure and their own weight. If a fluid is
flowing horizontally and along a section of
a streamline, where the speed increases it
can only be because the fluid on that
section has moved from a region of higher
pressure to a region of lower pressure; and
if its speed decreases, it can only be
because it has moved from a region of
lower pressure to a region of higher
pressure. Consequently, within a fluid
flowing horizontally, the highest speed
occurs where the pressure is lowest, and
the lowest speed occurs where the pressure
is highest. The principle is based on the
Bernoulli Equation where each term can be
interpreted as a form of pressure.
Calculation of velocity
Air Velocity
(1)
where
= Sensed pressure
difference (velocity pressure) in inches of
water column
D = Air density in lbs./ft.3
Cp = Pitot tube coefficient:
0.84
Air Density=
(2)
where
= Barometric pressure in
inches of mercury
T = Absolute Temperature
Flow in cubic feet per minute
equals duct cross sectional area in square
feet x air velocity in feet per minute. With
dry air at 29.9 inches of mercury, air
velocity can be read directly from
temperature correction charts on reverse.
Centres of
areas
Rectangular
Areas
National Conference on Research Advances in Communication, Computation, Electrical Science and Structures (NCRACCESS-2015)
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III BLOCK DIAGRAM
A pressure sensor measures pressure,
typically of gases or liquids. Pressure is an
expression of the force required to stop a
fluid from expanding, and is usually stated
in terms of force per unit area. A pressure
sensor usually acts as a transducer; it
generates a signal as a function of the
pressure imposed. Pressure sensors are
used for control and monitoring in
thousands of everyday applications.
Pressure sensors can also be used to
indirectly measure other variables such as
fluid/gas flow, speed, water level, and
altitude
PIC
MICROCONTROLLER
LCD DISPLAY
PRESSURE
SENSOR
(INPUT)
ALARM
PRESSURE
SENSOR
(OUTPUT)
(OUTPUT)
Figure2 Block diagram of pressure sensor
for leakage detection A.PRESSURE SENSING
This is where the measurement of
interest is pressure, expressed as
a force per unit area. This is useful in
weather instrumentation, aircraft,
automobiles, and any other machinery that
has pressure functionality implemented.
B. FLOW SENSING
This is the use of pressure sensors
in conjunction with the venturi effect to
measure flow. Differential pressure is
measured between two segments of a
venturi tube that have a different aperture.
The pressure difference between the two
segments is directly proportional to the
flow rate through the venturi tube. A low
pressure sensor is almost always required
as the pressure difference is relatively
small.
C.LEAK TESTING
A pressure sensor may be used to
sense the decay of pressure due to a system
leak. This is commonly done by either
comparison to a known leak using
differential pressure, or by means of
utilizing the pressure sensor to measure
pressure change over time.
IV EXPERIMENTAL RESULTS
The leakages in boiler rectangular
duct are minimized by using pressure
sensor. The figure below shows the
different level of pressure sensing. An LED
is often small in area (less than 1 mm2) and
integrated optical components may be used
to shape its radiation pattern. LEDs have
many advantages over incandescent light
sources including lower energy
consumption, longer lifetime, improved
physical robustness, smaller size, and faster
switching.
Liquid crystal displays (LCDs) have
materials which combine the properties of
both liquids and crystals. The LCD’s are
lightweight with only a few millimetres
thickness. Since the LCD’s consume less
power, they are compatible with low power
electronic circuits, and can be powered for
long durations.
The Proteus schematic capture
module lies at the heart of the system,
and is far more than just another
schematics package. It combines a
powerful design environment with the
ability to define most aspects of the
drawing appearance. The ISIS editor
consists of three main areas.
i. Editing window.
ii .Object selector and
iii Overview window
National Conference on Research Advances in Communication, Computation, Electrical Science and Structures (NCRACCESS-2015)
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In this figure two types of pressure
sensors are used. One is used as input
pressure and another one is used as output
pressure sensor. When the output level is
same as input level LED will glow. The
results were obtained by using LCD
display
In this figure the output pressure sensor
decreases while comparing with input
pressure sensor. Hence LED will turn OFF.
It helps to identify the leakages in boiler
rectangular duct.
IV PERFORMANCE ANALYSIS
The leakages are determined by using
pressure sensor. Different levels are
calculated and displayed by using LCD
display.
S.no
Pressure
sensor
(input)
Pressure
sensor
(output)
LED
ON/OFF
state
1
2.3
2.4
Off
2
2.8
2.8
On
3
2.8
2.6
Off
Table1 Different levels of pressure
sensor
V.CONCLUSION
In this paper, the leakages in boiler
rectangular duct were determined by using
pressure sensor. The leakages are occurred
due to many environmental facts. When the
leakage occurs in boiler duct it will be
detected by sensor. By using pressure
sensor, it would control the pressure level
in boiler rectangular duct. When the
pressure level increases, boiler tube will
brake; the leakages were detected by
pressure sensor and displayed by using
LCD display. The results were obtained by
using proteus 7 simulation software .In
future, an array of Pitot tubes can be used
to indicate fluid flow velocity by
measuring the difference between the static
and dynamic pressures in fluids. Pitot tube
is one of the simplest flow sensors it is
used in a wide range of flow measurement
applications. Temperature sensors are used
to measure different levels of temperature.
Pitot tubes are used to measure air flow in
pipes, ducts, stacks and liquid flow in
pipes.
VI REFERENCES
[1] Aime Lay-Ekuakille, Giuseppe Vendramin,
and Amerigo Trotta,Robust Spectral Leak
Detection of Complex Pipelines Using Filter
Diagonalization Method” ieee sensors
journal, vol. 9, no. 11, November 2009.
[2] B. Widarsson, E. Dotzauer, “Bayesian
network based early-warning for leakage in
Recovery boiler” –ATE (2007).
[3] “Idaho State University’s College of
Technology” Energy Systems Engineering
Technology- module 4 flow measurements.
[4] J. L. Martins de Carvalho, Gerhard Jank,
and J. Milhinhos “An LPV Modelling and
Identification Approach to Leakage
Detection in High Pressure Natural Gas
Transportation Networks” IEEE
transactions on control systems technology,
vol. 19.No. 1, January 2011.
[5] J. P. du Plessis and M. R. Collins “A new
definition for laminar flow entrance lengths
of straight ducts” n&o journal September
1992 .
[6] Kun Wang, Heng Lu, Lei Shu, and Joel J. P.
C. Rodrigues “A Context-Aware System
Architecture for Leak Point Detection in the
Large-Scale Petrochemical Industry” IEEE
Communications Magazine June 2014.
[7] Liansuo An, PengWang, AugustoSarti,
Fabio Antonacci, Jie Shi “Hyperbolic boiler
tube leak location based on quaternary
acoustic array” L. An et al. / Applied
Thermal Engineering 31 (2011).
[8] Nariman Sepehri, and Amin Yazdanpanah
Goharrizi “Internal Leakage Detection in
Hydraulic Actuators Using Empirical Mode
National Conference on Research Advances in Communication, Computation, Electrical Science and Structures (NCRACCESS-2015)
ISSN: 2348 - 8379 www.internationaljournalssrg.org Page 7
Decomposition and Hilbert Spectrum” ieee
transactions on instrumentation and
measurement, vol. 61, no. 2, February 2012.
[9] S. Shahul Hamid D. Najumnissa Jamal
Murshitha Shajahan “Automatic Detection
and Analysis of Boiler Tube Leakage
System” International Journal of Computer
Applications (0975 – 8887) Volume 84 –
No 16, December 2013.
[10] Sanghyo lee,Am cho Jihoon kim,Changdon
kee “Wind Estimation and Airspeed
Calibration using a UAV with a Single-
Antenna GPS Receiver and Pitot Tube” ieee
transactions on aerospace and electronic
systems vol. 47, no. 1 january 2011.
[11] Tabish Alam, R.P. Saini J.S. Saini “Use of
turbulators for heat transfer augmentation in
an air duct” journal T. Alam et al.
Renewable Energy 62 (2014) 689-715.
.
National Conference on Research Advances in Communication, Computation, Electrical Science
and Structures (NCRACCESS-2015)
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GENETIC ALGORITHM BASED POWER SYSTEM STABILISER FOR
SINGLE MACHINE INFINITE BUS SYSTEM
K.Kalaiselvan ME,
Assistant Professor, EEE,
Bharathiyar Institute of Engineering for
Women,
Attur(Tk), Salem(Dt), Tamil Nadu, India.
K.C.Kavitha ME,. Assistant professor ,EEE
Bharathiyar Institute of Engineering for
Women,
Attur(Tk), Salem(Dt), TamilNadu, India.
ABSTRACT: Power system stabilizers are now
routinely used in the industry to damp out power
system oscillations, over a wide range of operating
conditions and disturbances. The principal role of
a power system stabilizer is to increase the
damping of oscillations of generator rotor by
control of its excitation with the help of auxiliary
stabilizer signals. Genetic algorithm is one of the
global search techniques to provide a powerful
tool for optimization problems. In this thesis, the
genetic algorithm optimization technique is
applied to design a robust power system stabilizer
with optimal state feedback control for single
machine infinite bus system. The simulation
results of the MATLAB coding employed show
the effectiveness and robustness of the proposed
controller (GAPSS) and their ability to provide
efficient damping of low frequency oscillations.
Keywords: Genetic Algorithm, Power system
stabilizer, State feedback controller, Optimum
feedback controller.
1. INTRODUCTION
Power system stabilizers (PSS) are added to
excitation systems of the generator to enhance the
damping of electric power systems during low-
frequency oscillations. Several methods are used
in the design of PSSs. Recently, several
researchers taking advantage of optimal control
techniques have used modern control methods.
These methods utilize a state-space representation
of the power system model to calculate a gain
matrix which, when applied as a state feedback
control, will minimize a given prescribed objective
function .In practice, not all of the states are
available for measurement. In the state feedback
method the optimal control law requires the design
of a state observer. This increases the
implementation cost and reduces the reliability of
the control system. There is
another disadvantage of the observer-based control
system. Even a slight variation in the model
parameters from their nominal values may result
in significant degradation of the closed-loop
performance. Hence it is desirable to opt for an
output feedback design. The state output feedback
problem is one of the most investigated problems
in control theory. The power system stabilizers are
added to the power system to enhance the damping
of the electric power system. The design of PSS
can be formulated as a optimal linear regulator
control problem whose solution is a complete state
control scheme. The implementation requires the
design of state estimators that consume large time.
Recently, advanced numerical computation
methods such as Artificial Neural Network
(ANN), Fuzzy Logic Systems (FLS) and Genetic
Algorithms (GA) have been applied to various
power system problems including PSS design.
Genetic algorithms are global search techniques
and provide the solution of optimization problem
by miming the mechanism of natural selection and
genetics. In view of the above, the main thrust of
the research work presented in this thesis is to
design a robust power system stabilizer whose
parameters are tuned through GA.
To deal with problem, control scheme uses only
some desired state variables such torque angle and
speed. The desired objectives in this paper are:
• Variations of the angular frequency ( ) can be
achieved to end value equal to zero in minimum
time.
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• Variations of the torque angle ( ) can be
achieved to end value equal to minimum value
in minimum time.
2. STUDY SYSTEM
fig.1.Single line diagram of SMIB
The system studied in this paper is own in fig.1. It
is a machine-infinite-bus power system. The
system study is described by following state space
representation:
Where
xT=[Δω Δδ Δe’q ΔeFD ΔVR ΔVE]
ω : Rotor speed
δ : Torque angle
e’q : Q axis component of voltage behind transient
reactance
VR : Regulator output voltage
VE : Exciter output voltage
u : Supplementary control voltage
Δ denotes deviation from operating point.
Where matrices A and B from [2], as follow:
3. OPTIMAL DESIGN
GAs are search techniques using the
mechanics of natural selection and natural genetics
for efficient global searches [8]. In comparison to
the conventional searching algorithms, GAs has
the following characteristics: (a) GAs work
directly with the discrete points coded by finite
length strings (chromosomes), not the real
parameters themselves;(h) GAs consider a group
of points (called a population size) in the search
space in every iteration, not a single point; (c) GAs
use fitness function information instead of
derivatives or other auxiliary knowledge; and (d)
GAs use probabilistic transition rules instead of
deterministic rules. Generally, a simple GA
consists of the three basic genetic operators: (a)
Reproduction; (h) Crossover; and (c) Mutation.
They are described as follows.
(a). Reproduction:
Reproduction is a process to decide how many
copies of individual strings should be produced in
the mating pool according to their fitness value.
The reproduction operation allows strings with
higher fitness value to have larger number of
copies, and the strings with lower fitness values
have a relatively smaller number of copies or even
none at all. This is an artificial version of natural
selection (strings with higher fitness values will
have more chances to survive).
(b). Crossover:
Crossover is a recombined operator for two
high4tnessstrings (parents) to produce two off
springs by matching their desirable qualities
through a random process. In this paper, the
uniform crossover method is adopted. The
procedure is to select a pair of strings from the
mating pool at random, then, a mark is selected at
random. Finally, two new strings are generated by
swapping all characters correspond to the position
of the mark where the bit is “1”. Although the
crossover is done by random selection, it is not the
same as a random search through the search space.
Since it is based on the reproduction process, it is
an effective means of exchanging information and
combining portions of high fitness solutions.
(c) Mutation:
Mutation is a process to provide an occasional
random alteration of the value at a particular string
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10
position. In the case of binary string, this simply
means hanging the slate of a bit from 1 to 0 and
vice versa. In this paper we provide a uniform
mutation method. This method is first to produce a
mask and select a string randomly, then
complement the selected string value correspond
to the position of mask where the bit value is “1”.
Mutation is needed because some digits at
particular position in all strings may be eliminated
during here production and the crossover
operations. So the mutation plays the role of a
safeguard in GAs. It can help GAs avoid the
possibility of mistaking a local optimum for a
global optimum. The GA includes five
fundamental parameters: (a) Population size,
which influences amount of search points in every
generation. The more population size in the GAs
will increase the efficiency of searching, but it will
time consuming; (b) Crossover probability, which
influences the efficiency of exchanging
information. In general, the crossover probability
between 0.6 and I; (c) Mutation probability, which
occur with a small probability in the GAs. In
general, the mutation probability under 0.1. A
large mutation probability in GAs will eliminate
the result of reproduction and crossover, which let
GAs become a random search; (d) Chromosome
length, which influences their solution of the
searching result.
The GAs with
longer
chromosome
length will have
the higher
resolution, but it
will increase the
search space;
(e) Generations,
which
influences the
searching time and searching result. The GAs with
larger search space and less population size, it
needs more generations for a global optimum.
For achieving the above desired objectives, the
system is analyzed under two cases.
1.Open loop system
2.Optimal state feedback control system
4. SYSTEM ANALYSIS
Open Loop System
The Eigen values of the study system
under open loop condition are obtained as follows:
Table 1.1. Eigen values of the open loop system
By analyzing the eigen values, the system is in stable stable
condition. But the response of system obtained is more
oscillatory. Hence the system takes more time to settle down.
Optimal State Feedback Controller
In this part, the genetic algorithm is used
for tuning the two weighted matrices are taken for
optimization. The fitness function used for
optimizing Q and R values in genetic algorithm is
as follows:
f (i)=106
/ (a1* max1 +a2* ax2+a3*tp1+a4*tp2 +a5*
ts1+a6*ts2+a7*Fin1+a8*Fin2)
where ai : system performance factor
max1,max2 : maximum function values
tp1, tp2 : time of peak values
Fin1, Fin2 : end values
As known, optimal controller design is based on
the optimal factor K that is given by
u (t) = -Kx(t) (i) -----(i)
K = R-1
BT
p (ii) ---- (ii)
pA+AT p+Q- p BR
-1 B
T p=0 (iii)
Where A and B are state matrices, Q and R are
weighted matrices and K is the optimal gain.
Equation (iii) represents the Riccati equation. In
this thesis, genetic algorithm is implemented here
to get the optimized value of Q and R. First
optimal feedback values were designed by creating
initial population size with 30 Chromosome.
Te following parameters of genetic algorithm used
are:
Population size =30
Crossover probability =0.8
Mutation probability =0.6
Generation =100
Forecasting step size =[0 20]
-0.263+i10.82
-0.263-i10.82
-8.169+i8.951
-8.169-i8.951
-2.885
-1.624
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11
In this thesis, the uniform method for
mutation and crossover is proposed. The desirable
values of controller are obtained. By knowing the
optimized value of Q and R and solving the
equation (iii), the value of p is obtained.
AQ=A-B*K
Then the optimal gain K can be calculated. Then
by substituting the value of K in the following
equation (ii), We get the state matrix of optimal
controller AQ is obtained. Also By using the pole-
placement technique, the desired roots required for
the design of PSS are determined. Then by
applying the genetic algorithm for optimizing the
system. Finally the response of the system is
analyzed based on the settling time.
5. RESULT
System response to step input are shown in
figure.2. for open loop case. Results show
instability of system in normal operation.
Figure 2. Output Response To Step Input For Open
Loop System
Settling time and final value have not been desired
for second input when optimum controller was
used to improvement transient characteristic of
stabilizer. However system stability has been
improved considerably.
Figure 3.Output Response To Step Input For
Optimal Controller
Figure 4. Output response to step input for state
feedback controller
6. CONCLUSION
In this paper, a Genetic Algorithm method is used
to design state and optimum feedback controllers
for improvement PSS transient characteristics. The
results for one-machine system is represented by
Genetic Algorithm as simulation studies and
compared with three control cases content open
loop, optimum state feedback controller. However,
the system is stabilized by optimum feedback
controller but results illustrated undesired settling
time and oscillations of final values as secondary
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and Structures (NCRACCESS-2015)
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12
input, slightly. As shown, state feedback controller
lead to suitable operation of power system
stabilizer for studied one-machine system and
represented more proportional results than
optimum feedback controller.
REFERENCES
1. Chian-Chuang Ding, King-Tan lee, Chee-Ming Tsai,
Tsong-Liang Huang.“ Optimal Design for Power
System Dynamic Stabilizer by Grey Prediction PID
Control”, IEEE Trans.Power App. Systems,pp279-
284,May 2002.
2. Demello F.P., P. J. Nolan,. Laskowaki T.F, Undrill, J.M
“Coordinate Application of Stabilizers in Multimachine
Power Systems, ”IEEE Trans.Syst.,Vol.PAS-99,pp.892-
901,Nov 1980.
3. Hui-Mei Wag, Tsong-Liang Huaug, Chi-Ming Tsai,
Che-Wei Liu.“Power System stabilizer design Using
Adaptive Back stepping Controller”. IEEE Trans.Power
App. Systems, pp1027-1030, Sep 2002.
4. Hsu Y.Y., Hsu C.Y.,”Design of a Proportional-Integral
Power system Stabilizer,”IEEE Trans.Power App.
Systems, Vol. PWRS-1, No.2, pp. 46-53,Feb1986.
5. Richard K. Warner, Ali Feliachi,”Application of a
Genetic Algorithm Technique to Control a Simple
Power System”, Proceedings of the 36 Proceedings of
the 36th Conference on Decision & Control, IEEE 1997.
6. Y.S.Zhou and L.Y.Lai, “Optimal Design of Fuzzy
Controllers by Genetic Algorithms” IEEE Trans.on
industry applications, Vol.36, No.1 January/February
2002.
National Conference on Research Advances in Communication, Computation, Electrical Science and
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THREE-LEG VSC AND A T-CONNECTED TRANSFORMER BASED
THREE PHASE FOUR WIRE DSTATCOM FOR DISTRIBUTION SYSTEM
K.C.Kavitha ME,.
Assistant professor ,EEE
Bharathiyar Institute of Engineering for Women,
Attur(Tk), Salem(Dt), TamilNadu, India.
D.Sangeetha ME,
Assistant professor ,EEE
Bharathiar Institute Of Engineering For Women,
Attur(Tk), Salem(Dt), TamilNadu, India.
ABSTRACT:-
Three-phase four-wire distribution systems are
used in commercial buildings, office buildings,
hospitals, etc. Most of the loads in these locations
are nonlinear loads and are mostly unbalanced
loads in the distribution system. This creates
excessive neutral current both of fundamental and
harmonic frequency and the neutral conductor gets
overloaded. The voltage regulation is also poor in
the distribution system due to the unplanned
expansion and the installation of different types of
loads in the existing distribution system.
In this thesis, a new three-phase four-wire
distribution static compensator (DSTATCOM)
based on a T-connected transformer and a three-leg
voltage source converter (VSC) is proposed for
power quality improvement. The T-connected
transformer connection mitigates the neutral current
and the three-leg VSC compensates harmonic
current and balances the load. Two single-phase
transformers are connected in T-configuration for
interfacing to a three-phase four-wire power
distribution system. The insulated gate bipolar
transistor (IGBT) based VSC is supported by a
capacitor and is controlled for the required
compensation of the load current. The dc bus
voltage of the VSC is regulated during varying load
conditions. The DSTATCOM is tested for
harmonic elimination, neutral current compensation
along with voltage regulation, and balancing of
linear loads as well as nonlinear loads. The
synchronous reference frame theory is used for the
control of the proposed DSTATCOM The
performance of the three-phase four wire
DSTATCOM is validated using MATLAB
software with its Simulink and power system block
set toolboxes
I INTRODUCTION
Electric power distribution network
becomes more increasingly important and plays an
essential role in power system planning. Three
phase four-wire distribution power system has been
widely used for supplying low-level voltage to
office building, commercial complexes,
manufacturing facilities, etc. The loads connected
to the three-phase four-wire distribution power
system may be either the single-phase or the three-
phase loads. Non-linear loads draw current that are
non-sinusoidal and thus create voltage drops in
distribution conductors that are non-sinusoidal.
Most of these loads have the nonlinear input
characteristic, which creates a problem of high
input current harmonics. The harmonic current will
pollute the power system and result in the problems
such as transformer overheats, rotary machine
vibration, degrading voltage quality, damaging
electric power components, medical facilities
malfunction, etc In order to meet the increasing
reactive power demands reactive power
compensation has been recognized as an efficient
and economic means of increasing power
transmission capability. To complete this challenge,
it requires careful design for power network
planning. There exist many different ways to do so.
However, one might consider an additional device
to be installed somewhere in the network. Such
devices are one of capacitor bank, shunt reactor,
series reactors, automatic voltage regulators and/or
recently developed dynamic voltage restorers,
distribution STATCOM (our focus), or
combination of them.
At present, a wide range of very flexible
controllers, which capitalize on newly available
power electronics components, are emerging for
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custom power applications. Among these, the
distribution static compensator and the dynamic
voltage restorer are most effective devices, both of
them based on the VSC principle. A DVR injects a
voltage in series with the system voltage and a D-
STATCOM injects a current into the system to
correct the voltage sag, swell and interruption. The
DSTATCOM has plenty of applications in low
voltage distribution systems aimed to improve the
quality and reliability of the power supplied to the
end-user.it can be used to prevent non-linear loads
from polluting the rest of the distribution system.
The rapid response of the DSTATCOM makes it
possible to provide continuous and dynamic control
of the power supply including voltage and reactive
power compensation, harmonic mitigation and
elimination of voltage sags and swells.
II CONTROL ALGORITHM OF DSTATCOM
There are many control schemes available for
control of shunt active compensators. The control
approaches available for the generation of reference
source currents for the control of VSC of
DSTATCOM for three-phase four-wire system are
instantaneous reactive power theory (IRPT)/PQ
theory, synchronous reference frame theory
(SRFT), power balance theory, instantaneous
symmetrical components based, etc. They are
described as follows:
Synchronous Reference Frame Theory
As the application of ac machines has
continued to increase over this century, new
techniques have been developed to aid in their
analysis. Much of the analysis has been carried out
for the treatment of the well-known induction
machine. The significant breakthrough in the
analysis of three-phase ac machines was the
development of reference frame theory. Using these
techniques, it is possible to transform the phase
variable machine description to another reference
frame. By judicious choice of the reference frame,
it proves possible to simplify considerably the
complexity of the mathematical machine model.
The synchronous reference frame
theory(SRFT) is based on the determination of the
instantaneous active and reactive currents(id and
iq).The SRFT creates a reference frame of
orthogonal axes that rotates at the supply
frequency(d-q system),that is, a synchronous
reference. This synchronism with the supply can be
achieved by a phase locked loop (PLL) connected
to the supply voltages or currents. In some
situations only the supply frequency is necessary
for applying the SRFT, so that, the supply phase is
not needed. In this rotating reference, the
fundamental stator current becomes dc values in the
id-iq currents that can be determined by some kind
of low-pass filter. In order to calculate the id and iq
currents, the invariant power Clarke transformation
is applied to the stator currents, followed by the
Park transformation, so that, the stator currents at
the a-b-c system are transferred to the α-β-0 system
and from the α-β-0 system to the d-q-0 system.
Equations (1) and (2) show the Park and the
invariant power Clarke transformations,
respectively.
(1)
(2)
Where θ is the phase angle of the phase voltage;
and the fundamental frequency unit vectors, sin(θ)
and cos (θ), are determined by the PLL.
The id and iq components can be both divided into
alternating (ac) and constant parts(dc),as shown
below.
(3)
After the Park transformation the
fundamental stator currents becomes the dc parts of
the id and iq currents(idˉ and iqˉ)and all the rest of
the harmonics become the ac parts of them(id ˜and
iq˜) with a frequency offset equal to the supply
frequency. Therefore, eliminating the ac parts of id
and iq, that is, id˜ and iq˜, the fundamental currents
at the d-q-0 system, that is , id˜
and iq˜, will last.
The elimination of ac parts of id and iq can be done
by some kind of low pass filter.
Once the idˉ and iqˉ currents were determined, they
must be transformed to the α-β-0 system by the
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inverse invariant power Clarke transformation as in
following equation (4).
(4)
And, so to the a-b-c system by the inverse invariant
power Clarke transformation in equation (5)
(5)
III SYSTEM CONFIGURATION AND
DESIGN
The schematic diagram of three-phase four-
wire compensated system is shown in Figure 4.1.
The compensator and the load are connected at a
point called as point of common coupling (PCC).
The load may be unbalanced and non-linear.
Figure. Schematic diagram of three-phase
four-wire compensated system
COMPENSATOR STRUCTURE AND MODELING
The proposed DSTATCOM consisting of a
three-leg VSC and a T-connected transformer is
shown in Fig. 2, where the T-connected transformer
is responsible for neutral current compensation. The
windings of the T-connected transformer are
designed such that the mmf is balanced properly
in the transformer. The VSC converts the dc
voltage across the storage device into a set of three-
phase ac output voltages. These voltages are in
phase and coupled with the ac system through the
reactance of the coupling transformer. Suitable
adjustment of the phase and magnitude of the D-
STATCOM output voltages allows effective control
of active and reactive power exchanges between the
D-STATCOM and the ac system. Such
configuration allows the device to absorb or
generate controllable active and reactive power.
Figure. Schematic diagram of VSC and T-
transformer Based DSTATCOM in Distribution
System
In the present work neutral clamped converter
topology is used to track the reference currents
using two-level voltage source converter as shown
in Figure4.2. The structure of two-level VSC
consists of six IGBT switches, each with anti-
parallel diodes and a dc storage capacitor.
In two-level inverter six IGBT switches
are used each with the dc storage capacitors are
used for VSC operation, they will discharge in due
course of time, due to switching losses in the
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compensator. The dc capacitor connected at the dc
bus of the converter acts as an energy buffer and
establishes a dc voltage for the normal operation of
the DSTATCOM system. If the voltage across any
of these capacitors falls below the peak of system
voltage, then the inverter will not track the
reference currents properly. Therefore for proper
operation of compensator, the total voltage across
the capacitor is to be maintained at the reference
voltage level. The ripple filter at the point of
common coupling (PCC) for reducing the high
frequency ripples. The high frequency ripple
voltage is due to the switching current of the VSC
of the DSTATCOM.
The required compensation to be provided by
the DSTATCOM decides the rating of the VSC
components. The data of DSTATCOM system
considered for analysis is shown in the Appendix.
The T-connected transformer mitigates the neutral
current and the three-leg VSC compensates the
harmonic current and reactive power, and balances
the load. The insulated gate bipolar transistor
(IGBT) based VSC is self-supported with a dc bus
capacitor and is controlled for the required
compensation of the load current. The selection of
interfacing inductor, dc capacitor, and the ripple
filter are given
in the following sections.
Capacitor Voltage
The minimum dc bus voltage of VSC of
DSTATCOM should be greater than twice the peak
of the phase voltage of the system. The dc bus
voltage is calculated as
Vdc =2√2VLL
√3m
(1)
where m is the modulation index and is considered
as 1, and VLLis the ac line output voltage of
DSTATCOM. Thus, Vdc is obtained as 677.69 V for
VLLof 415 V and is selected as 700 V.
DC Bus Capacitor
The value of dc capacitor (Cdc) of VSC of
DSTATCOM depends on the instantaneous energy
available to the DSTATCOM during transients. The
principle of energy conservation is applied as
½ Cdc [(v2
dc)-(V2dc1)]= 3V (a I) t
(2)
where Vdc is the reference dc voltage and Vdc1 is the
minimum voltage level of dc bus, a is the
overloading factor, V is the phase voltage, I is the
phase current, and t is the time by which the dc bus
voltage is to be recovered. Considering the
minimum voltage level of the dc bus, Vdc1 =690 V,
Vdc = 700 V, V = 239.60 V, I = 27.82 A, t = 350 μs,
a = 1.2, the calculated value of Cdc is 2600 μF and
is selected as 3000 μF.
AC Inductor
The selection of the ac inductance (Lf) of
VSC depends on the current ripple icr,p-p switching
frequency fs, dc bus voltage(Vdc), and Lf is given as
Lf=√3mVdc
12a fs icr,p-p(3)
where m is the modulation index and a is the
overload factor. Considering, icr,p-p = 5%, fs= 10
kHz, m = 1, Vdc = 700 V, a = 1.2, the Lf value is
calculated to be 2.44 mH. A round-off value of Lf
of 2.5 mH is selected in this investigation.
Ripple Filter
A low-pass first-order filter tuned at half
the switching frequency is used to filter the high-
frequency noise from the voltage at the PCC.
Considering a low impedance of 8.1 Ω for the
harmonic voltage at a frequency of 5 kHz, the
ripple filter capacitor is designed as Cf= 5 μF. A
series resistance (Rf) of 5 Ω is included in series
with the capacitor (Cf). The impedance is found to
be 637 Ω at fundamental frequency, which is
sufficiently large, and hence, the ripple filter draws
negligible fundamental current.
Design of the T-connected Transformer
The T-connected transformer is used in the
three-phase distribution system for different
applications. But the application of T-connected
transformer for neutral current compensation is
demonstrated for the first time. Moreover, the T-
connected transformer is suitably designed for
magnetic motive force (mmf) balance. The T-
connected transformer mitigates the neutral current
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and the three-leg VSC compensates the harmonic
current and reactive power, and balances the load.
Figure.Winding diagram of T-Connected
Transformer
The T-connected windings of the
transformer not only provide a path for the zero-
sequence fundamental current and harmonic
currents but also offer a path for the neutral current
when connected in shunt at point of common
coupling(PCC). Under unbalanced load, the zero-
sequence load-neutral current divides equally into
three currents and takes a path through the T-
connected windings of the transformer. The current
rating of the windings is decided by the required
neutral current compensation. The voltages across
each winding are designed as shown shortly.
The phasor diagram gives the following relations
to find the turn’s ratio of windings. If Va1 and Vb1
are the voltages across each winding and Vais the
resultant voltage, then
Va1 = K1Va (4)
Vb1 = K2Va (5)
where K1and K2are the fractions of winding in the
phases. Considering |Va | = |Vb | = Vand Va1 =
Vacos 30◦, Vb1 = Vasin 30◦, then from (4) and (5),
we gets, K1 = 0.866 and K2 = 0.5.
The line voltage is
Vca= 415 V
Va= Vb= Vc=415/√3= 239.60 V
(6)
Va1 = 207.49 V, Vb1 = 119.80 V.
(7)
Hence, two single-phase transformers of rating 5
kVA, 240 V/120 V and 5 kVA, 208 V/208 V
are selected.
SYNCHRONOUS REFERENCE FRAME
IMPLEMENTATION
The control approaches available for the
generation of reference source currents for the
control of VSC of DSTATCOM for three-phase
four-wire system are instantaneous reactive power
theory (IRPT), synchronous reference frame theory
(SRFT),unity power factor (UPF) based,
instantaneous symmetrical components based, etc.
The SRFT is used in this thesis for the control of
the DSTATCOM.
Figure .Control Block of DSTATCOM in
Distribution System
A block diagram of the control scheme is
shown in Fig. 4.4. The load currents (iLa, iLb, iLc),
the PCC voltages (vSa, vSb, vSc), and dc bus voltage
(vdc) of DSTATCOM are sensed as feedback
signals. The load currents from the a–b–c frame are
first converted to the α–β–o frame and then to the
d–q–o frame using
(8)
where cos θ and sin θ are obtained using a three-
phase phase locked loop (PLL). A PLL signal is
obtained from terminal voltages for generation of
fundamental unit vectors for conversion of sensed
currents to the d–q–o reference frame. The SRF
controller extracts dc quantities by a low-pass filter,
and hence, the non-dc quantities (harmonics) are
separated from the reference signal. The d-axis and
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q-axis currents consist of fundamental and
harmonic components as
(9)
(10)
CONTROL OF DSTATCOM
UPF Operation of DSTATCOM
The control strategy for reactive power
compensation for UPF operation considers that the
source must deliver the mean value of the direct-
axis component of the load current along with the
active power component current for maintaining the
dc bus and meeting the losses (iloss) in
DSTATCOM. The output of the proportional-
integral (PI) controller at the dc bus voltage of
DSTATCOM is considered as the current (iloss) for
meeting its losses
iloss (n) = iloss (n−1) + Kpd (vdc(n)− vdc(n−1)) + Kidvdc(n)
(11)
where vdc(n) = v*dc− vdc(n) is the error between the
reference(v*dc) and sensed (vdc) dc voltages at the
nth sampling instant. Kpdand Kidare the proportional
and integral gains of the dc bus voltage PI
controller.
The reference source current is therefore
i*d= id dc + iloss(12)
The reference source current must be in phase with
the voltage at the PCC but with no zero-sequence
component. It is therefore obtained by the
following reverse Park’s transformation with i*das
in (12) and i*qand i*0 as zero
(13)
Zero-Voltage Regulation (ZVR) Operation of
DSTATCOM
The compensating strategy for ZVR
operation considers that the source must deliver the
same direct-axis component i*d, as mentioned in
(12) along with the sum of quadrature-axis current
(iq dc) and the component obtained from the PI
controller (iqr) used for regulating the voltage
at PCC. The amplitude of ac terminal voltage (VS)
at the PCC is controlled to its reference voltage (V
* S) using the PI controller. The output of PI
controller is considered as the reactive component
of current (iqr) for zero-voltage regulation of ac
voltage at PCC. The amplitude of ac voltage (VS) at
PCC is calculated from the ac voltages (vsa, vsb, vsc)
as
VS = (2/3)1/2
(Vsa2+Vsb
2+Vsc
2)
1/2
(14)
Then, a PI controller is used to regulate this voltage
to a reference value as
iqr (n) = iqr(n−1) + Kpq(vtc(n)− vtc(n−1)) + Kiq vtc(n)
where vte(n) = V * S − VS(n) denotes the error between
reference(V * S ) and actual (VS(n) ) terminal voltage
amplitudes at the nth sampling instant. Kpqand
Kiqare the proportional and integral gains of the dc
bus voltage PI controller. The reference source
quadrature - axis current is
i*q= iqdc + iqr.
The reference source current is obtained by reverse
Park’s transformation using (13) with i*d as in (12)
and i*q as in (16) and i*0 as zero
Current-Controlled pulse width modulation
(PWM) Generator
In a current controller, the sensed and
reference source currents are compared and a
proportional controller is used for amplifying
current error in each phase before comparing with a
triangular carrier signal to generate the gating
signals for six IGBT switches of VSC of
DSTATCOM.
SIMULATION AND RESULTS
SIMULINK
SIMULINK is a companion program to
MATLAB. It is an interactive system for simulating
non linear dynamic systems. It is graphical mouse
driven program that allows modeling of system by
drawing a block diagram on the screen and
manipulating it dynamically. It can work with
National Conference on Research Advances in Communication, Computation, Electrical Science and
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linear, non-linear, continuous and discrete time,
multivariable and multi rate system.
Block sets are add-ins to simulink that
provides additional libraries of blocks for
specialized applications like communications,
signal processing and power systems.
SIMULATION OF DSTATCOM SYSTEM
The three-leg VSC and the T-transformer
based DSTATCOM connected to a three-phase
four-wire system is modeled and simulated using
the MATLAB with its Simulink and Power System
Blockset toolboxes. The system data are given in
Appendix. The MATLAB based model of the
three-phase four-wire DSTATCOM is developed.
The control algorithm for the DSTATCOM
is also modeled in MATLAB. The reference source
currents are derived from the sensed PCC voltages
(vsa, vsb, vsc), load currents (iLa, iLb, iLc) and the dc
bus voltage of DSTATCOM (vdc). A pulse width
modulated (PWM) current controller is used over
the reference and sensed source currents to generate
the gating signals for the IGBTs of the VSC of the
DSTATCOM.
The performance of the three-phase four-
wire DSTATCOM is demonstrated for power factor
correction and voltage regulation along with
harmonic reduction, load balancing and neutral
current compensation. The developed model is
analyzed under varying loads and the results are
shown below.
OUTPUT WAVEFORMS OF DSTATCOM
WITH LINEAR LOAD UNDER ZVR
OPERATION
5.5. OUTPUT WAVEFORMS OF DSTATCOM
WITH NON-LINEAR LOAD UNDER ZVR
OPERATION
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INFERENCES
The source neutral current is observed as nearly
zero, and this verifies the proper compensation. It is
also observed that the dc bus voltage of
DSTATCOM is able to maintain close to the
reference value under all disturbances. The
amplitude of PCC voltage is maintained at the
reference value under various load disturbances,
which shows the ZVR mode of operation of
DSTATCOM. The dc bus voltage of DSTATCOM
is maintained at nearly its reference value under all
load disturbances
5.7 OUTPUT WAVEFORMS OF DSTATCOM
WITH LINEAR LOAD UNDER UPF
OPERATION
5.9. OUTPUT WAVEFORMS OF DSTATCOM
WITH NON-LINEAR LOAD UNDER UPF
OPERATION
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CONCLUSION
The performance of a new topology of three-
phase four-wire DSTATCOM consisting of three-
leg VSC with a T-connected transformer has been
demonstrated for neutral current compensation
harmonic elimination, and load balancing. The T-
connected transformer has mitigated the source-
neutral current. The voltage regulation and power
factor correction modes of operation of the
DSTATCOM have been observed and are as
expected. The dc bus voltage of the DSTATCOM
has been regulated to the reference dc bus voltage
under all varying loads. The performance of
DSTATCOM is verified under linear and non-
linear load conditions. The Simulink result shows
that the DSTATCOM compensates the harmonic
current and balances the load.
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ANALYSIS OF GENETIC ALGORITHM TO SOLVE ECONOMIC
LOAD DISPATCH PROBLEM IN THE POWER SYSTEM
R.KAYALVIZHI
PG Scholar,
M.E-Power Systems Engineering
Muthayammal College of Engineering, India
ABSTRACT- In this paper, an efficient and practical
real-coded genetic algorithm (GAs) has been proposed
for solving the economic load dispatch (ELD) problem.
The objective is to minimize the total generation fuel
cost and keep the power flows within the security limits.
For each problem of optimization in genetic algorithms
(GAs) there are a large number of possible encodings.
The efficiency of the GAs is increased as there is no
need to convert chromosomes to the binary type, less
memory is required. There is no loss in precision. The
proposed technique improves the quality of the solution
and speed of convergence of the algorithm. The Coding
are written and executed the values are plotted in graph
for different values of MW loading.
Index term- Economic Load Dispatch, Fuel cost, Genetic
Algorithm, MW loading
I.INTRODUCTION
Improve the reliability and efficiency of power systems,
new communication technologies, and distributed energy
Sources, and demand response programs have been intro-
duced. These efforts are mainly motivated by the increasing
Costs of fossil fuels, environmental changes, and energy
security concerns coupled with investments in wind and
solar generation to replace conventional CO -emitting
energy sources. The increased flexibility of the power sys-
tem results in a higher level of complexity for economic
dispatch (ED) problems.
Real-time dispatch is, in general, computed in two stag-
es. In the first stage, a unit commitment (UC) problem is
solved to select generating units to meet the expected load
during each hour. In the second stage, an ED problem is
solved to compute the power outputs of the committed
units for meeting the load. This ED decision takes place
minutes to hours ahead of the time of implementation.
Recent work has explored UC for planning purposes to
accommodate generation/load forecasting uncertainties,
mostly based on standard commercial solvers. However,
the location-based marginal prices (LMPs) are found by
solving the corresponding ED problem with fixed unit
commitment decisions.
II. SCOPE
The economic dispatch (ED) problem is one of the most
important operational functions of the modern clay energy
management system. The purpose of the ED is to find the
optimum generation among the existing units, such that the
total generation cost is minimized while simultaneously
satisfying the power balance equations and various other
constraints in the system. The literature of the ED problem
and its solution methods are surveyed. However, it is rea-
lized that the conventional techniques become very compli-
cated when dealing with increasingly complex dispatch
problems, and are further limited by their lack of robustness
and efficiency in a number of practical applications.
Recently, a global optimization technique known as GAs
which is a kind of the probabilistic heuristic algorithm has
been studied to solve the power optimization problems. The
GAs may find the several sub-optimum solutions within a
realistic computation time. The efficiency and the robust-
ness of the proposed GAs are demonstrated by test func-
tions. Then the GAs with simulated non uniform arithmetic
crossover, elitism and a non uniform mutation are applied
to ED problem. III.CHALLENGES IN MEETING POWER DEMAND
The main objective oof Economic load dispatch of
electric power generation is to schedule the commited ge-
nerating units output so as to meet the load demand at min-
imum operating cost, while satisfying all the unit and the
system equality and inequality constraints
For the purpose of economic dispatch studies,online
generators are represented by functions that relates their
production cost to their power output.Quadratic cost func-
tions are used to model generator in order to simplify the
mathematical formulation of the problem and to allow
many of the convensional optimization technique to be
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used. IV. OPTIMIZATION TECHNIQUE FOR THE ECO-NOMIC DISPATCH IN POWER SYSTEM OPERA-TION
The optimal economic operation of their electric net-
works while considering the challenges of increasing fuel
costs and increasing demand for electricity. The dynamic
economic dispatch (DED) occupies important place in a
power system’s operation and control. It aims to determine
the optimal power outputs of on-line generating units in
order to meet the load demand and reducing the fuel cost.
The nonlinear and non convex characteristics are more
common in the DED problem. Therefore, obtaining a op-
timal solution presents a challenge. In the proposed system,
genetic algorithm(GA) – a recently introduced population-
based technique – is utilized to solve the DED problem. We
demonstrate 3 units and 6 units generating system for simu-
late the maximize power output and minimize the fuel cost.
4.1.1 NEED FOR ECONOMIC LOAD DISPATCH
1.Planning for Tomorrow’s Dispatch
i. Scheduling generating units for each hour of the
next day’s dispatch
a. Based on forecast load for the next day
b. Select generating units to be running and
available for dispatch the next day (operat-
ing day)
ii. Recognize each generating unit’s operating limit,
including its:
iii. Ramp rate (how quickly the generator’s output can
be changed)
iv. Maximum and minimum generation levels
v. Minimum amount of time the generator must run
a. Minimum amount of time the generator
must stay off once turned off
2. Recognize generating unit characteristics, including:
i. Cost of generating,
ii. Its variable operating costs (fuel and non-fuel),
iii. Variable cost of environmental compliance,
iv. Start-up costs,
3. Reliability Assessment.
4. Dispatching the Power System Today.
4.1.2 OVER VIEW OF ECONOMIC LOAD DIS-PAATCH
Economic dispatch is the short-term determination of
the optimal output of a number of electrical genera-
tion facilities, to meet the system load, at the lowest possi-
ble cost, while serving power to the public in a robust and
reliable manner. The Economic Dispatch Problem is solved
by specialized computer software which should honor the
operational and system constraints of the available re-
sources and corresponding transmission capabilities. This is
defined as "the operation of generation facilities to produce
energy at the lowest cost to reliably serve consumers, re-
cognizing any operational limits of generation and trans-
mission facilities.
4.1.3 Solution methods for Economic load dispatch
Some of the algorithms are given below, they are, 1. Artificial neural networks 2. Genetic algorithms 3. Evolutionary algorithms 4. Particle swarm optimization 5. Ant colony optimization 6. Fuzzy logic 7. Other biological systems. 4.1.5 Algorithm Used In Proposed System
The algorithm used here is the Genetic algorithm(GA)
is well-known stochastic methods of global optimization
based on the evolution theory of Darwin. They have suc-
cessfully been applied in different real-world applica-
tions.With this proposed technique we will reduce the fuel
cost and losses.
V. GENETIC ALGORITHM
5.1.1 Introduction
GA was originally developed for solving unconstrained
problems. Recently, many variants of GAs have been de-
veloped for solving constrained nonlinear programming.
Most of these methods were based on penalty formulations
that transform (1) into an unconstrained function Fm(PG,rk)
(6), consisting of a sum of the objective and the constraints
weighted by penalties, and use GAs to minimize
Fm(PG,rk). GAs, unlike strict mathematical methods, have
the apparent ability to adapt to non linear ties and disconti-
nuities commonly found in power systems.
The basic idea behind GAs is to mathematically imitate the
evolution process of nature. The algorithms are based on
the evaluation of a set of solutions, called population. The
population is treated with genetic operations. At the itera-
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tion i the population Xi consist of a number of N individu-
al’s xj, that is, solutions, where N is called a population
size. The population is initialized by randomly generated
individuals.
The individuals can be encoded using either binary or real
numbers. We use the latter because of their popularity. Each
individual xj = (x1, …,xn) is a vector of variables. Each
variable is a real number. The suitability of an individual is
determined by the value of the objective function, to be
called a fitness function.
A new population is generated by the genetic operations
selection, crossover and mutation. Parents are chosen by
selection and new off springs are produced with crossover
and mutation. All these operations include randomness. The
success of the optimization process is improved by elitism
where the best individuals of the old population are copied
as such to the next population.
5.1.2 Optimization with GA
For each problem of optimization in GAs there are a
large number of possible encodings. Although binary repre-
sentation is usually applied to power optimization prob-
lems, in this letter we use a GA switch is a modified GAs
employing real valued vectors for representation of the
chromosomes. The use of real valued representation in the
GAs has a number of advantages in numerical function op-
timization over binary encoding.
The efficiency of the GAs is increased as there is no need
to convert chromosomes to the binary type, less memory is
required, there is no loss in precision by discretization to
binary or other values, and there is greater freedom to use
different genetic operators.
For the real valued representation, the k-th
Chromosome Ck can be defined as follows
Ck=[Pk1, Pk2 ,…,Pkn] k=1,2,…,popsize
Where pop size means population size and Pki is the gener-
ation power of the i-th unit at k-th chromosome. Reproduc-
tion involves the creation of new offspring from the mating
of two selected parents or mating pairs. It is thought that
the crossover operator is mainly responsible for the global
search property of the GA. A non-uniform arithmetic cros-
sover operator was introduced into the GAs.
5.1.3 Economic load dispatch with GA
The economic dispatch problem, which is used to mi-
nimize the cost of production of real power, can generally
be stated as follows:
Economic load dispatch problem is allocating loads to
plants for minimum cost while meeting the constraints. It
is formulated as an optimization problem of minimizing the
total fuel cost of all committed plant while meeting the de-
mand and losses .The variants of the problems are numer-
ous which model the objective and the constraints in differ-
ent ways.
The basic economic dispatch problem can described ma-
thematically as a minimization of problem of minimizing
the total fuel cost of all committed plants subject to the
constraints. n
i
iPMinimize1
i )(F
…….(1)
)( ii PF is the fuel cost equation of the ‘i’th plant. It is the
variation of fuel cost ($ or Rs) with generated power
(MW).Normally it is expressed as continuous quadratic
equation. maxmin2
,)( iiiiiiiiiij PPPcPbPaPF …..(2)
The total generation should meet the total demand and
transmission loss. The transmission loss can be deter-
mined form either Bmn coefficients or power flow.
l
n
i
i PDP1 ………(3)
n
i
n
j
jiijl PPBP
……..(4)
GAs is a probabilistic search technique, which generates
the initial parent vectors distributed uniformly in intervals
within the limits and obtains global optimum solution over
number of iterations. The implementation of GAs is given
below. The initial population is generated after satisfying
the equation. The elements of parent vectors ( PGi ) are the
real power outputs of generating units distributed uniformly
between their minimum and maximum limits.
The fitness function is used to transform the cost function
value into a measure of relative fitness. The fitness function
is given in equation.
1. Select a reference plant. For better convergence
chose a plant which has maximum capacity and range.
In this program It is considered as plant 1. The refer-
ence plant allocation is fixed by the equations
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(A3&A4).
2. Convert the constrained optimization problem as an
unconstrained problem by penalty function method.
n
i
n
j
jiij
n
i
i
n
i
i PPBDPabsP
Minimize
1 111
i )(*1000)(F
……..(5)
3. The allocation minimum fuel cost and transmission
losses can be determined
Table 5.1.3 Genetic algorithm optimization
POWER DEMAND
UNIT1 OPTIMIZED POWER
500
354.3868
600
274.4185
700
320.9227
800
355.078
900
380.207
1000
406.8564
Fig 5.1.3 Graph for Genetic algorithm optimiza-
tion
5.1.4 Algorithm for GA
Step 1
Input the value of load demand, power demand and the
values of cost coefficients, ai, bi, ci where i=1, 2…n.
Step 2
Input the values of Bmn coefficients in the non linear equa-
tion
Step 3
Update the loss coefficients and find the total demand and
the incremental fuel cost.
Step 4
Assume Pi = 0 for i = 1, 2...n
Step 5
Fix the limit for generating plant and solve equation itera-
tively for Pi’s.
Step 6
Check if test unit 1 converges to load demand at its limit.
Similarly check the conversion of the other plants.
Step 7
Check if power balance equation is satisfied and find fuel
cost and emission when all the 6 test units are converged.
The above said steps are explained in following flowchart
5.1.5 Flowchart for GA
StartA
initialization
Evaluate Pl and update PD
Substitute Bmn coefficients in the non linear equations&
find incremental fuel cost
Determine optimal Power limit
of plant 1,2...6.
Are plant1,2,3,4 load de-
mand converged to power
limit?
Determine optimal Power limit
of plant 1,2...6.
Substitute Bmn coefficients in the non linear equations&
find incremental fuel cost
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NO
A YES
YES
NO
NO
NON
YES
Fig 5.1.5 Flow chart for ELD using GA
VI SIMULATION AND RESULT
6.1 PROBLEM FORMULATION
The ED problem may be expressed by minimizing the
fuel cost of generator units under constraints. Depending on
load variations, the output of generators has to be changed
to meet the balance between loads and generation of a
power system. The power system model consists of n gene-
rating units already connected to the system.
The ED problem can be expressed as:
Where,
ai, bi and ci are the cost coefficients of the it generator.
NG is the number of generators including the slack bus.
PGi is the real power output of the i-th generator (MW).
Fi(PGi) is the operating cost of unit i ( $/h). 6.2 CONSTRAINTS
Subjects to the following constraints,
GAs is a general stochastic optimization algorithm that was
originally developed for solving unconstrained problems.
By applying an exterior penalty function we transform a
constrained non-linear ED problem into an unconstrained
A
A
Are plants 5,6 load
demand converged to
power limit?
Find Fcost and Ecost
Stop
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problem
6.6.2 Input datas
1. Loss co-efficients.
2. Data matrix (Fuel cost co-efficient(ai, bi and c i) and
plant limits).
ai = measure of losses in the system.
bi = represents the fuel cost.
ci = includes salary and wages, interests and deprecia-
tion.
3. Load demand(D).
6.6.3 Simulation results
F = 1.0813e+04 P1 = 386.3605 116.8379 201.3102 64.3634 99.1567 50.4517 Pl = 18.4805
VIII. CONCLUSION
In this paper, an approach based on a genetic algorithm
has been successfully presented and applied to the genera-
tion cost in electric power network to obtain the optimum
solution of economic dispatch (ED). Operators are used in
LAGRANGIAN to generate a set of solutions for this prob-
lem. LAGRANGIAN method is most useful for large pow-
er systems, it LAGRANGIAN ve well results and it is
much faster and more effective than iterative method. Me-
thods are compared for solving an economic dispatch prob-
lem with two generators. Test results have shown GA algo-
rithm can provide highly optimal solutions and reduces the
computation time than those with the iterative method. An
advantage of the GA solution is the flexibility it provides in
modeling both time dependent and coupling constants [10].
Another advantage of the GA approach is the ease with
which it can handle arbitrary kinds of constraints and ob-
jectives.
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REFERENCE
[1]. AlKalaani,Y., F.E. Villaseca, and F.Jr. Renovich,(1996) Fuel-
Constrained Unit Commitment, IEEE Transactions on
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[2]. Ana V. and el al,(2003) Using GRASP to Solve the Unit
Commitment Problem, Annals of Operations Research
120(1); 117-132.
[3]. Beltran, C.,(2002) Unit Commitment by Augmented Lagra-
gian Relaxation: Testing Two Decomposition Approaches,
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[4]. C.L. TSENG, C.L.,(2000) Solving the Unit Commitment
Problem by a Unit De commitment Method, Journal of opti-
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[5]. Hobbs, B.F and et al.(2001), The Next Generation of Electric
Power Unit Commitment Models, Kluwer: Academic Pub-
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[6]. Jorge V.,(2002) A Seed Memetic Algorithm for Large Unit
Commitment Problems, Journal of Heuristics 8(2):173-195.
[7]. Padhy, N.P.,Unit Commitment(2004)- A Biblography Survey,
IEEE Trans. Power Systems, Vol. 19, no. 2, pp. 1196-1205.
[8]. Robert N., and Werner R.,(2002) A two-Stage Planning Mod-
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[9]. Wood, W.G.,(1982) Spinning Reserve Constrained Static
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[10]. Wood and Wollenberg,(1996) Power Generation, Operation
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National Conference on Research Advances in Communication, Computation, Electrical Science and
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PERFORMANCE ANALYSIS OF PHOTOVOLTAIC PUMPING
SYSTEM FOR BLDC MOTOR USING FUZZY LOGIC
CONTROLLER
J.P. Srividhya M.E., (PhD)., Department of EEE,
Assistant professor,
SKP Engineering College,
Thiruvannamalai, India
ABSTRACT:-A new converter for photovoltaic water
pumping system without the use of batteries was
introduced. The converter is designed to drive a bldc
motor directly coupled from Photovoltaic source. The
use of Bldc motor presents a better solution to the
commercial dc motor water pumping system. The
advanced (or) developed system is based on dc-dc
converter of sepic converter. We use an intelligent
control method (P&O) for searching the maximum
power point (MPP). This method uses a fuzzy logic
controller applied to a drive a DC–DC converter to an
optimal operating point using PV Panel’s measured
variables.
Key-Words: - Pumping system, photovoltaic,
MPPT, BLDC, Fuzzy logic controller
1. INTRODUCTION:-
One of the major concerns in the power sector
is the day-to-day increasing power demand,but the
unavailability of enough resources to meet the power
demand using the conventional energy sources.
Demand has increased for renewable sources of
energy to be utilized along with conventional
systems to meet the energy demand. Renewable
sources like wind energy and solar energy are the
prime energy sources which are being utilized in this
regard. The continuous use of fossil fuels has caused
the fossil fuel deposit to be reduced and has
drastically affected the Environment depleting the
biosphere and cumulatively adding to global
warming. Solar energy is abundantly available that
has made it possible to harvest it and utilize it
properly. Solar energy can be a standalone
generating unit or can be a grid connected generating
unit depending on the availability of a Grid nearby.
Thus it can be used to power rural areas where the
availability of grids is very low. Another advantage
of using solar energy is the portable operation
whenever wherever necessary.
E.Suresh,
Department of PSE,
PG Student,
SKP Engineering College,
Thiruvannamalai, India
In order to tackle the present energy crisis one has to
develop an efficient manner in which power has to be
extracted from the incoming solar radiation. The
power conversion mechanisms have been greatly
reduced in size in the past few years. The
development in power electronics and material
science has helped engineers to come up very small
but powerful systems to withstand the high power
Demand. But the disadvantage of these systems is the
increased power density. The trend has set in for the
use of multi-input converter units that can effectively
handle the voltage fluctuations. But due to high
production cost and the low efficiency of these
systems they can hardly compete in the competitive
markets as a prime power generation source. The
constant increase in the development of the solar
cells.
Manufacturing technology would definitely make
the use of these technologies possible on a wider
basis than What the scenario is presently. The use of
the newest power control mechanisms called the
Maximum Power Point Tracking (MPPT) algorithms
has led to the increase in the efficiency of operation
of the solar modules and thus is effective in the field
of utilization of renewable sources of energy. In
order to extract the maximum power of the PV array,
the classical implementation of the maximum power
point tracking (MPPT) in stand-alone systems is
generally accomplished by the series connection of a
dc–dc converter between the PV array and the load
or the energy storage element. The operation
principle, theoretical analysis, design methodology,
and experimental results of a laboratory prototype of
the MPPT system are presented in this paper.
Permanent magnet DC motors use a
mechanical commutator and brushes to achieve the
commutation. However, BLDC motors adopt Hall
Effect sensors in place of mechanical commutator
and brushes. The stators of BLDC motors are the
coils, and the rotors are the permanent magnets the
stators develop the magnetic fields to make the rotor
rotating. Hall Effect sensors detect the rotor position
as the commutating signals. Therefore, the BLDC
motors use permanent magnets instead of coils in the
armature and so do not need the brushes. As the rotor
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position is detected by incremental encoder then the
Hall Effect sensors used to detect rotor position for
BLDC motor
The conversion of solar light into electrical energy
represents one of the most Promising and challenging
energy technologies, in continuous development,
being clean, silent and reliable, with very low
maintenance costs and minimal ecological impact.
Solar energy is free, practically inexhaustible, and
involves no Polluting residues or greenhouse gas
emissions.
The conversion principle of solar light into
electricity, called Photo- Voltaic or PV Conversion,
is not very new, but the efficiency improvement of
the PV conversion equipment is still one of top
priorities for many academic and/or industrial
research groups all over the world.
In this paper, an intelligent control technique using
fuzzy logic Controller is associated to an MPPT
controller in order to improve energy conversion
efficiency of a PV standalone water pumping system
2 .DESIGN OF PUMPING SYSTEM
The following figure describes elements constituting
the water pumping system figure
2.1 PV array
Fig.2.1 Block diagram of the BLDC
water pumping system
This is the most important element since it provides
the electric power needed from the water pumping
System; we chose the PV panel Majority of
commercial PV cells are fabricated from Silicon. A
PV cell is essentially a large diode that produces a
voltage when exposed to incident light. It may be
considered to be a light-emitting diode ―run
backward the analogy is similar to a heat engine and
a refrigerator.
The PV generator is a non-linear device and is
usually described by its equivalent circuit and the I-V
characteristics; the electrical model of a solar cell is
composed of a diode, two resistances and a current
generator. The relationship between the voltage V
(V) and the current I (A) am given by
Where IL, I0 and I are the photocurrent, the inverse
Saturation current and the operating current, RS and
RP are series and parallel resistances, respectively,
which depend on the incident solar radiation and the
cell temperature A=KT/q is the diode quality factor.
K and q are Boltzmann constant and electronic
charge respectively.
The current and the voltage parameters of the PV
Generator is: Ipv =I and Vpv = nsNsV, where ns, Ns
are the numbers of series cells in the panel and of the
series panels in the generator. The PV generator
consists of solar cells connected in series and parallel
fashion to provide the desired voltage and current
required by the load.
2.2 PHOTOVOLTAIC MODULE
The voltage generated by a single solar cell
is very low, around 0.5V. So, a number of solar cells
are connected in both series and parallel connections
to achieve the desired output. In case of partial
shading, diodes may be needed to avoid reverse
current in the array. Good ventilation behind the
solar panels are provided to avoid the possibility of
less efficiency at high temperatures.
Fig.2.2 Simplified circuit diagram of a solar PV
cell
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Shows typical I-V characteristics for increasing
insolation levels of the used PV array. The short
circuit current varies in proportion to the insolation
level, while the open circuit voltage is approximately
constant. Consequently, the extracted electric power
will increase accordingly. Each curve has a
maximum power point, which is the optimal
operating point for an efficient use of the solar array
3.DC-DC CONVERTER
3.1 SINGLE-ENDED PRIMARY-INDUCTOR
CONVERTER
Single-Ended Primary-Inductor Converter (SEPIC) is a type of allowing the electrical potential
(voltage) at its output to be greater than, less than, or
equal to that at its input; the output of the SEPIC is
controlled by the duty cycle of the control transistor.
A SEPIC is similar to a traditional buck-boost
converter, but has advantages of having non-inverted
output (the output has the same voltage polarity as
the input), using a series capacitor to couple energy
from the input to the output (and thus can respond
more gracefully to a short-circuit output), and being
capable of true shutdown: when the switch is turned
off, its output drops to 0 V, following a fairly hefty
transient dump of charge.
SEPICs are useful in applications in which a battery
voltage can be above and below that of the
regulator's intended output. For example, a single
lithium ion battery typically discharges from 4.2
volts to 3 volts; if other components require 3.3 volts,
then the SEPIC would be effective.
Fig.3.1 Schematic of SEPIC
4. CONTROLLER
4.1 MPPT METHODS
There are a large number of algorithms that are able
to track MPPs. Some of them are simple, such as
those based on voltage and current feedback, and
some are more complicated. There are many MPPT
methods available in the literature; the most widely-
used techniques are described in the following such
as
Perturbation And Observation (P&O)
Incremental Conductance (Inc Cond)
Method.
Constant Voltage Method
Parasitic Capacitance Method
Fuzzy Logic Controller and etc.
4.2 FUZZY LOGIC CONTROL SYSTEM
In contrast to conventional control techniques, fuzzy
logic control (FLC) is best utilized in complex ill-
defined processes that can be controlled by a skilled
human operator without much knowledge of their
underlying dynamics.
The basic idea behind FLC is to incorporate the
"expert experience" of a human operator in the
design of the controller in controlling a process
whose input – output relationship is described by
collection of fuzzy control rules (e.g., IF-THEN
rules) involving linguistic variables rather than a
complicated dynamic model. The utilization of
linguistic variables, fuzzy control rules, and
approximate reasoning provides a means to
incorporate human expert experience in designing
the controller.
FLC is strongly based on the concepts of fuzzy sets,
linguistic variables and approximate reasoning
introduced in the previous chapters. This chapter will
introduce the basic architecture and functions of
fuzzy logic controller, and some practical application
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examples. A typical architecture of FLC is shown
below, which comprises of four principal comprises:
a fuzzifier, a fuzzy rule base, inference engine, and a
defuzzifier.
Fig.4.1 A Fuzzy Logic System
If the output from the defuzzifier is not a control
action for a plant, then the system is fuzzy logic
decision system. The fuzzifier has the effect of
transforming crisp measured data (e.g. speed is10
mph) into suitable linguistic values (i.e. fuzzy sets,
for example, speed is too slow).The fuzzy rule base
stores the empirical knowledge of the operation of
the process of the domain experts. The inference
engine is the kernel of a FLC, and it has the
capability of simulating human decision making by
performing approximate reasoning to achieve a
desired control strategy. The defuzzifier is utilized to
yield a non fuzzy decision or control action from an
inferred fuzzy control action by the inference engine.
4.3 STRUCTURE OF A FUZZY
CONTROLLER
4.4 OPERATION OF FUZZY
CONTROLLER
5. VOLTAGE SOURCE INVERTER
The main objective of static power
converters is to produce an ac output waveform from
a dc power supply. These are the types of waveforms
required in adjustable speed drives (ASDs),
uninterruptible power supplies (UPS), static var
compensators, active filters, flexible ac transmission
systems (FACTS), and voltage compensators, which
are only a few applications. For sinusoidal ac
outputs, the magnitude, frequency, and phase should
be controllable. According to the type of ac output
waveform, these topologies can be considered as
voltage source inverters (VSIs), where the
independently controlled ac output is a voltage
waveform. These structures are the most widely used
because they naturally behave as voltage sources as
required by many industrial applications, such as
adjustable speed drives (ASDs), which are the most
popular application of inverters. Similarly, these
topologies can be found as current source inverters
(CSIs), where the independently controlled ac output
is a current waveform. These structures are still
widely used in medium-voltage industrial
applications, where high-quality voltage waveforms
are required. Static power converters, specifically
inverters, are constructed from power switches and
the ac output waveforms are therefore made up of
discrete values. This leads to the generation of
waveforms that feature fast transitions rather than
smooth ones. For instance, the ac output voltage
produced by the VSI of a standard ASD is a three-
level waveform. Although this waveform is not
sinusoidal as expected, its fundamental component
behaves as such. This behaviour should be ensured
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by a modulating technique that controls the amount
of time and the sequence sused to switch the power
valves on and off. The modulating techniques most
used are the carrier-based technique (e.g., sinusoidal
pulse width modulation, SPWM), the space-vector
(SV) technique, and the selective-harmonic-
elimination (SHE) technique.
Fig.5.1 Three-phase VSI topology.
The inverter provides three-phase system
voltages variable in amplitude and frequency to
operate with variable loads and frequency
(from0.1 up to 1 time the rated frequency) The
current is modulated sinusoidally to obtain a
high efficiency. The pulse frequency is maximal
2kHz.The phase voltage can be expressed as
follows
6. BLDC MOTOR
Brushless DC (BLDC) motors are synchronous
motors with permanent magnets on the rotor and
armature windings on the stator. Hence, from a
construction point of view, they are the inside-out
version of DC motors, which have permanent
magnets or field windings on the stator and armature
windings on the rotor. A typical BLDC motor with
12 stator slots and four poles on the rotor the most
obvious advantage of the brushless configuration is
the removal of the brushes, which eliminates brush
maintenance and the sparking associated with them.
Having the armature windings on the stator helps the
conduction of heat from the windings. Because there
are no windings on the rotor, electrical losses in the
rotor are minimal. The BLDC motor compares
favorably with induction motors in the fractional
horsepower range. The former will have better
efficiency and better power factor and, therefore, a
greater output power for the same frame, because the
field excitation is contributed by the permanent
magnets and does not have to be supplied by the
armature current. These advantages of the BLDC
motor come at the expense of increased complexity
in the electronic controller and the need for shaft
position sensing. Permanent magnet (PM) excitation
is more viable in smaller motors, usually below 20
kW. In larger motors, the cost and weight of the
magnets become
Fig.6.1 Three-phase BLDC motor
6.2 MACHINE CONSTRUCTION
BLDC motors are predominantly surface-magnet
machines with wide magnet pole-arcs and
concentrated stator windings. The design is based on
a square waveform distribution of the air-gap flux
density waveform as well as the winding density of
the stator phases in order to match the operational
characteristics of the self-controlled inverter
7. CENTRIFUGAL PUMP MODEL
The centrifugal pump applies a load torque
proportional to the square of the rotational speed of
the motor
The performances (Q ', H' and P ') are given in terms
of the speed using the following relationships
National Conference on Research Advances in Communication, Computation, Electrical Science and
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Fig.7.1 Liquid flow path inside a centrifugal pump
8. RESULTS AND DISCUSSION
Show that output voltage DC/DC sepic
converter we note that the system follows the
variations of irradiation
Fig.8.1 Simulation Model
8.2 STATOR CURRENT VS TIMES
Fig.8.2 Shows That the Bldc Motor Stator
Current D (A)
8.3 STATOR VOLTAGE VS TIMES
Fig.8.3Shows That the Bldc Motor Stator voltage
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16-10
-5
0
5
10
15
TIME (S)
CU
RR
EN
T (
A)
<Stator current is_d (A)>
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16-5
0
5
10
15
20
25
30
35
Time (S)
voltage (
V)
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8.4 STATOR CURRENT VS. TIMES
Fig.8.4 Shows That the Bldc Motor Stator three
phase current
9. CONCLUSION
This project presents the simulation work of a
Photovoltaic array feeding a BLDC motor using
pumping system. SEPIC converter and voltage
source inverter were used as interface between PV
module and the BLDC motor. Fuzzy logic controller
algorithm was used to track the maximum power
from PV module. The simulation works of these
circuits were carried out in the MATLAB/simulink
software. The SEPIC converter gives the constant
output voltage using the FLC and the BLDC motor
can running at constant speed and sliding mode
technique used to estimate the rotor position.
10. REFERENCES
[1] Xin Wang and Aiguo Patrick Hu “An Improved
Maximum Power Point Tracking Algorithm For
Photovoltaic Systems”, Australasian Universities Power
Engineering Conference , Brisbane, Australia, 2004.
[2] F. M. González-Longatt: Model of photovoltaic module
in Matlab. In 2do congreso iberoamericano de estudiantes
de ingenierıacute;a eléctrica, electrónica y computación,
iicibelec, 2005, pp. 1–5.
[3] K.Rajashekara, A.Kawamura, et al, “Sensorless Control
of AC Motor Drivers,” IEEE press, 1996. January 2005.
[4]N.Femia,G.Petron,G.Spagnuolo,andM.Vitelli,
“Optimization of perturb and observe maximum power
point tracking method,” IEEE Trans. Power Electron., vol.
20, no. 4, pp. 963–973, Jul. 2005.
[5] J. A. Abu-Qahouq, H. Mao, H. J. Al-Atrash, and I.
Batarseh, “Maximum efficiency point tracking (MEPT)
method and digital dead time control implementation,”
IEEE Trans. Power Electron., vol. 21, no. 5, pp. 1273–
1281, Sep. 2006.
[6] V. Salas, E. Olias, A. Barrado and A. Lazaro, „Review
of the Maximum Power Point Tracking Algorithms for
Stand-Alone Photovoltaic Systems‟, Solar Energy Materials
& Solar Cells, Vol. 90, N°11, pp. 1555 – 1578,Jul. 2006.
[7] W. Xiao, W. G. Dunford, P. R. Palmer, and A. Capel :
Regulation of photovoltaic voltage. In IEEE Trans. Ind.
Electron., vol. 54, no. 3, pp. 1365– 1374, Jun. 2007.
[8] R. B. Darla, “Development of Maximum Power Point
Tracker for PV Panels Using SEPIC Converter” in
proceeding on Telecommunications Energy conference,
INTELEC 2007, pp 650 655.
[9]Chee Wei Tan, Tim C. Green and Carlos A. Hernandez-
Aramburo“Analysis of Perturb and Observe Maximum
Power Point Tracking Algorithm for Photovoltaic
Applications”, 2nd IEEE International Conference on
Power and Energy (PECon 08), December 1-3, 2008, Johor
Baharu, Malaysia.
[10]Z. Yan, L. Fei, Y. Jinjun, and D. Shanxu: Study on
realizing MPPT by improved incremental conductance method with variable step-size. In Proc. IEEE ICIEA, Jun.
2008, pp. 547–550.
[11] M. G. Villalva, J.R. Gazoli, E. R. Filho,
“Comprehensive Approach to Modeling and Simulation of
PV Arrays”, IEEE Transactions on Power Electronics, Vo.
24, No. 5, pp 1198-1208, May 2009.
[12]E. E. Jimenez-Toribio, A. A. Labour-Castro and
F.M.RoDriguez, “Sensorless Controll of SEPIC and Cuk
Converters for DC Motors using Solor Panels” in
proceeding on Electrical Machines and Drives conference,
IEMDC-09, 2009, pp 1503-1510.
[13] M. Salhi, and R. El-Bachtiri, Maximum Power Point
Tracking controller for PV systems using a PI regulator
with boost DC/DC converter, ICGST-ACSE journal, vol. 8,
issue III, pp. 21-27, 2009.
[14] M. Salhi, and R. El-Bachtiri, A Maximum Power
Point Tracking Photovoltaic System using a Proportional
Integral Regulator, Science Academy Transactions on
Renewable Energy Systems Engineering and Technology,
vol. 1, pp. 37-44, June 2011.
[15] M. Salhi, A. Saadi, and R. El-Bachtiri,
"Dimensionnement d‟un convertisseur boost DC/DC pour
la poursuite du point de puissance maximale d‟un système
photovoltaïque", 2ème congrès de l‟Association Marocaine
de Thermique (AMT), 18-19 Avril, Casablanca, Morocco,
2012.
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16-15
-10
-5
0
5
10
15
Time(S)
CU
RR
EN
T (A
)
National Conference on Research Advances in Communication, Computation, Electrical Science and Structures
(NCRACCESS-2015)
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FOUR AREA INTERCONNECTED SYSTEM ON LOAD FREQUENCY
CONTROL USING FIREFLY ALGORITHM
S. Priyadharshini ME.,
Assistant professor ,EEE
Bharathiyar Institute of Engineering for women
Attur(Tk), Salem(Dt), Tamil Nadu, India.
K.Kalaiselvan ME.,
Assistant Professor, EEE,
Bharathiyar Institute of Engineering for Women,
Attur(Tk), Salem(Dt), Tamil Nadu, India.
ABSTRACT: The paper deals with optimal tuning of a
PID controller for a load frequency control of four
areas Power system using Firefly algorithm. The
proposed approach has superior feature, including
easy implementation, stable convergence
characteristics and very good computational
performances efficiency. The main objective is to
obtain a stable, robust and controlled system by
tuning the PID controller using Firefly algorithm.
The interconnected four area LFC system is modeled
and simulated using MATLAB-SIMLINK
environment and the PID control parameters are
tuned based on FA algorithm. By comparison with
the conventional technique, the effectiveness of the
anticipated scheme is confirmed. Hence the results
establishes that tuning the PID controller using the
firefly optimisation technique gives less over shoot,
system is less sluggish. Optimization technique finds
the best parameters for controller and designed
controller are an optimal controller. The simulated
results are obtained for different load configurations
of the Firefly algorithm based controller and this
indicate that the better control performance in terms
of overshoot and settling time can be achieved by
choosing PID among the other considered classical
controllers.
Keywords: LFC, PID, Power System Control, Firefly
algorithm.
I. INTRODUCTION
In a power system, the generating electric power
unit must satisfy the load demand to all consumers in the
system with desired qualities. The main objective of
power system control is to maintain the continuous
balance between electrical generation and varying load
demand and the associated system losses while system
frequency and voltage level are maintained constant. The
load variations in the power system affect the quality of
power. If the power demand is more than the generated
power, system frequency will decrease and if the power
demand is lesser than the generated power, system
frequency will increase affecting the real power of the
system [1]. Hence the balance of the power system gets
disturbed. To supply the load demand without giving
much constrain to a single system and to improve the
reliability, power systems are interconnected and power
is exchanged between the systems over the tie-lines by
which they are connected. An approach on the
“Evolutionary Computation based Four-Area Automatic
Generation Control in Restructured Environment”[4].
load frequency control (LFC) is a very important issue
in power system operation and control for supplying
sufficient and both good quality and reliable power.
A new methodological approach on “Optimizing
power flow of AC–DC power systems using artificial
bee colony algorithm”[16]. PID controller improves the
transient response of a system by reducing the
overshoot, and by shortening the settling time of a
system. Although new methods are proposed for tuning
the PID controller, their usage is limited due to
complexities arising at the time of implementation.
Since, Firefly Optimization algorithm is an optimization
method that finds the best parameters for controller in
the uncertainty area of controller parameters and
obtained controller is an optimal controller. The
objective of this study is to investigate the load
frequency control and inter area tie-power control
problem for a four-area power system taking into
consideration the uncertainties in the parameters of
system [1]. An optimal control scheme based firefly
Algorithm (FA) method is used for tuning the
parameters of this PID controller. The proposed
controller is simulated for a four-area power system. To
show effectiveness of proposed method and also
compare the performance of these four controllers,
several changes in demand of the four areas
simultaneously are applied. Simulation results indicate
that Firefly algorithm based controllers guarantee the
good performance under various load
conditions.
II.FOUR AREA POWER SYSTEM:
Power systems have variable and complicated
characteristics and comprise different control parts and
also many of the parts are nonlinear.These parts are
connected to each other by tie lines and need
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controllability of frequency and power flow.
Interconnected multiple-area power systems can be
depicted by using circles. A simplified four area
interconnected power system used in this study is shown
in Fig. 1.
Fig 1.Simplified interconnected power system diagram.
In an interconnected power system, different
areas are connected with each other tie-lines. When the
frequencies in two areas are different, a power exchange
occurs through the tie-line that connected the two areas,
where ∆Ptieij is tie-line exchange power between areas i
and j, and Tij is the tie-line synchronizing torque
coefficient between area i and j as shown in fig.2.it can
see that the tie-line power error is the integral of the
frequency difference between the two areas.
Fig 2.Block diagram of the tie line
Where, Δfi & Δfj: are the two areas interconnected.
Tij: the tie line synchronizing torque coefficient
ΔPtie : tie line power exchange between areas I and j.
Laplace transformation of the tie line is given by,
ΔPtie12(s)=2ΠT0/s(Δf1(s)-Δf2(s))
(1) The system state-space model can be represented as
ẋ =Ax+Bu
y=Cx
(2)
Where, system matrix A, input matrix B, state matrix x,
control matrix u and output matrix C
u=[u1 u2 u3 u4]T
y = [y1 y2 y3 y4] T=[Δf1 Δf2 Δf3 Δf4]
T
x = [Δf1 ΔPT1 ΔPG1 ΔPC1 ΔPtie1
Δf2 ΔPT2 ΔPG2 ΔPC2 ΔPtie2
Δf3 ΔPT3 ΔPG3 ΔPC3 ΔPtie3
Δf4 ΔPT4 ΔPG4 ΔPC4 ΔPtie4] T
(3)
For the four area considered in this study, the
conventional integral controller was replaced by a PID
controller with the following structure.
K(s) = KP +KI / S+ KDS
Where KP is the proportional gain, KI is the integral
gain, and KD is the differential gain, respectively. The
PID controllers in both areas are considered to be
identical.
Fig. 3. Block diagram of a four-area power system.
The control signal for PID controller can be given in the
following equation.
Ui(s) = -k(s)*ACEi(s)
(4)
Now a performance index can be defined by adding the
sum of squares of cumulative errors in ACE, hence
based on area control error a performance index J can be
defined as:
J=
0
2^)( dtACE i
(5)
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Based on this performance index J optimization problem
can be stated as: Minimize J subjected to:
KP min
≤ KP ≤ KPmax
KI
min≤ KI ≤ KI
max
KD
min≤ KD≤ KD
max
(6)
IV METHODOLOGY OF FIREFLY ALGORITHM:
Nature-inspired methodologies are among the
most powerful algorithms for optimization problems.
Firefly algorithm is a novel nature-inspired algorithm
inspired by social behavior of fireflies. Fireflies are one
of the most special, captivating and fascinating creature
in the nature. There are about two thousand firefly
species, and most fireflies produce short and rhythmic
flashes. The main part of a firefly‟s flash is to act as a
signal system to attract
other fireflies. Firefly-inspired algorithms use the
following three idealized rules:
(1) All fireflies are unisex which means that they are
attracted to other fireflies regardless of their sex;
(2) the degree of the attractiveness of a firefly is
proportion to its brightness, thus for any two flashing
fireflies, the less brighter one will move towards the
brighter one and the more brightness means the less
distance between two fireflies. If there is no brighter one
than a particular firefly, it will move randomly;
(3) the brightness of a firefly is determined by the value
of the objective function. For a maximization
problem, the brightness of each firefly is proportional to
the value of the objective function. In case of
minimization problem, brightness of each firefly is
inversely proportional to the value of the objective
function. Based on the effectiveness of the firefly
algorithm in optimizing continuous problems, it is
predictable that this algorithm can also be modified to
solve discrete optimization problems in an effective
manner. In general, firefly algorithm incorporates three
important strategies which are given as follows.
A.ATTRACTIVENESS
In the firefly algorithm, the main form of
attractiveness function β(r) can be any monotonically
decreasing functions such as the following generalized
form:
β(ɼ )=β0e-γrm
, m ≥ 1
(7)
where r is the distance between two fireflies, β0 is the
initial attractiveness of firefly and γ is a absorption
coefficient.
B. DISTANCE BETWEEN FIREFLIES:
The distance between any two fireflies p and q at
positions xp and xq respectively, can be defined as a
Cartesian or Euclidean distance as follows:
rpq=||Xp-Xq||=√∑k-1d(Xp,s-Xq.s)
2
(8)
where xp,s is the sth component of the spatial coordinate
of the pth firefly and d is the total number of dimensions.
Also q ε {1,2,. . . ,Fn} is randomly chosen index.
Although q is determined randomly, it
has to be different from p. Here Fn is the number of
fireflies. For other applications such as scheduling, the
distance can be any of the suitable forms, not necessarily
the Cartesian distance.
C. MOVEMENT OF FIREFLY:
The movement of a firefly p, when attracted to
another more attractive (brighter) firefly q, is determined
by
X1=Xp+β(ɼ )˟(Xp-Xs)+α(rand-1\2 )
(9)
The third term introduces randomization with „α‟ being
the randomization parameter and „„rand‟‟ is a random
number generated uniformly distributed
between 0 and 1.
Fig 4 .Firefly behaviour
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Fig 5.Flowchart of firefly algorithm
V.ALGORITHM
In the firefly algorithm, the optimization process
depends on the brightness of the fireflies and the
movement of fireflies towards their brighter
counterparts. Every firefly is attracted to the other
depending on brightness because the fireflies are all
unisexual according to the first assumption about
artificial fireflies. The following section describes the
pseudo code.
Step 1: Define an initialize objective function f(x), x =
(x1,..xd)
Step 2: Generate initial population of fireflies xi (i = 1, 2,
..., n)
Step 3: Determine light intensity for xi by calculating
f(xi)
Step 4: Define light absorption coefficient γ
While t < Maximum Generation
Make a copy of the generated firefly
population for move function
For i = 1 : n all n fireflies
For j = 1 : i all n fireflies
If (Ij > Ii ),
Move fireflies i and j according to
attractiveness
Evaluating new solutions and updating
light intensity for next iteration
End if End for j
End for i
Sorting the fireflies to find the present best
End while Begin post process on best results obtained
During the iterative process, the brightness of one firefly
is compared with the others in the swarm and the
difference in the brightness triggers the movement. The
distance travelled depends on the attractiveness between
the fireflies. During the iterative process the best
solution thus far is continuously updated and the process
goes on until certain stopping conditions are satisfied.
After the iterative process comes to a halt the best
solution of the evaluation is determined and the post
process is initiated to obtain the results. The flowchart
diagram is shown in Figure 5.
VI. RESULTS AND SIMULATIONS:
In this section different comparative cases are
examined to show the effectiveness of the proposed FA
method for optimizing Controller parameters. table 1
gives the optimum values of the overshoot, Table 2 gives
the values of the under shoot and the table 3 gives the
values of the settling time. The simulation results are
shown in Figs. 6,7 in
this study.
Table 1: comparison of PID and optimisation of the four
area power system for overshoot
AREA OVER SHOOT (HZ)
PID FIREFLY
Area 1 0.005 0.0055
Area 2 0.013 0.005
Area 3 0.013 0.003
Area 4 0.006 0.01
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Table 2: comparison of PID and optimisation of the
four area power system for undershoot
Table 3: comparison of PID and optimisation of the
four area power system for Settling time
The four area system can be simulated using the
MATLAB-SIMULINK environment. The simulation
results shown in Figs. 4 and 5 gives the frequency
response and step load change using PID controller and
the firefly algorithm based optimisation technique. Thus
the simulation results shows the following,
Fig 6: Frequency response and step load change of
four area system with PID controller
Fig 7.Frequency response and step load change of
four area system using optimisation technique
CONCLUSION:
Firefly algorithm is proposed in this four area power
system to tune the parameters of PID for LFC.
Optimisation -PID controller is suggested to generate
good quality and reliable electric energy. Simulation
results emphasis that the designed FA tuning PID
controller is robust in its operation and gives a good
damping performance both for frequency and tie line
power deviation compared to conventional controller.
AREA
UNDER
SHOOT (HZ)
PID FIREFLY
Area 1 -0.021 -0.012
Area 2 -0.023 -0.012
Area 3 -0.023 -0.013
Area 4 -0.032 -0.031
AREA
SETTLING TIME (SEC)
PID FIREFLY
Area 1 45 36
Area 2 66 50
Area 3 63 46
Area 4 52 37
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Also, these controllers have a simple architecture and the
potentiality of implementation in real time environment.
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APPENDIX
AREA 1,2,3&4
Tp1=20sec, Kp1=120, TT1=0.3sec, TG1=0.08sec,
R1=2.4
T12=T13=T14=T21=T23=T31=T32=T41=0.545
T24=T34=T42=T43=0,BS1=BS2=BS3=BS4=0.425:
a12=a41=a23=a31=-1
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Structures (NCRACCESS-2015)
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Step-Down Converter with Efficient ZCS Operation with Load
Variation
[1]K.KALAISELVAN,
[2] S.SHANMUGAPRIYA, M.SWATHI, K.THULASIMANI
[1]Assistant Professor, [2] UG Student, Department of Electrical and Electronics Engineering,
Bharathiyar Institute of Engineering for Women
ABSTRACT— A step-down converter is presented.
It is composed of an auxiliary switch, a diode, and
a coupled winding to the buck inductor in the
conventional buck converter. By transferring the
buck-inductor current to the coupled winding in a
very short period, the negatively built-up leakage
inductor current of the buck winding guarantees
the zero-current switching (ZCS) operation of the
buck switch in all load conditions. Furthermore,
since the negatively built-up leakage inductor
current is minimized after the zero current of the
buck switch is achieved, the unnecessary current
build-up and the conduction loss are minimized.
Therefore, efficient ZCS operation with load
variation is achieved. The operation principle, ZCS
analysis, design, and experimental results of the
proposed converter are presented.
Contents — Buck converter, power conversion,
power electronics, zero-current switching (ZCS).
I. INTRODUCTION
THE STEP-DOWN power-conversion
technique is widely used in power sources for
microprocessors, battery chargers, LED drivers, solar-
power regulators, and so on. For these applications,
high power density, high efficiency, and low noise are
the driving force in power converter research. High
power density can be achieved by increasing
switching frequency since magnetic and capacitive
elements can be designed in small sizes in high
frequencies. However, increasing switching frequency
greatly increases switching power dissipation due to
the hard switching of the power switch in
conventional converters. Moreover, this increases
switching noise. Therefore, to achieve high power
density with high efficiency and low noise, the soft-
switching technique is a prerequisite in modern
switching converters. The choice of the soft-switching
technique depends on the type of switching device,
and the zero-current switching (ZCS) technique is
more preferable in MOSFET switches since it
eliminates the capacitive loss of a power switch
during the turn-on transition. Many types of ZCS
converters have been presented. The resonant type of
ZCS technique is presented. These converters well
utilized the parasitic components of the power switch
with additional resonant components. However,
higher voltage and current stresses are common in
these converters. The active resonant tank is used in to
achieve the ZCS of the power switch. However, this
converter induces conduction loss in the added
inductor and capacitor. ZCS is achieved in by
resonating the current between the interleaved
inductors and the parasitic capacitors of the switches.
However, conduction loss in the auxiliary inductor
occurs all the time, which reduces the efficiency of the
converter. In the auxiliary active ZCS technique is
used. However, since the reduced current is delivered
to output during the on time of the auxiliary switch,
this converter increases the dc value of the buck
inductor and therefore increases its conduction loss. In
the ZCS buck converter with a coupled inductor is
presented. The characteristics of the coupled-inductor
converter are also presented. A simple ZCS buck
converter with a coupled inductor is presented in with
small additional components. However, a large
current is built up in the coupled winding in light-load
conditions and therefore greatly increases conduction
loss. In addition, less power is transferred to the
output during the switch-off period, which increases
the dc value of the buck inductor current. A ZCS
bidirectional converter is presented in by applying two
auxiliary switches and a coupled inductor. However,
additional diodes are necessary to implement
unidirectional auxiliary switches, thereby increasing
component counts. The other ZCS converters
presented in have their own good characteristics and
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drawbacks as mentioned earlier such as the large
number of components, increased device stress,
circulating current, complex structure, and so on.
A new buck converter with efficient ZCS
operation with load variation is presented. The target
application of the proposed converter is in solar-
power regulator modules. The solar-power regulator is
a module that converts solar to electrical energy to
power satellite buses. The proposed converter is
composed of an auxiliary switch, a diode, and a
coupled winding in the conventional buck inductor.
During a very short turn-on period of the auxiliary
switch, the buck inductor current is transferred to the
coupled winding and the leakage inductor current is
built up negatively. It guarantees the ZCS of the
switch in all load conditions through the negative
leakage inductor current. Furthermore, since the
negatively built-up leakage inductor current is
minimized after the zero current of the buck switch is
achieved, the unnecessary current build-up and
conduction loss are minimized as the load goes down,
which indicates the efficient ZCS operation with load
variation. The operation principle, analysis, design
example, and experimental results of the proposed
converter are presented in this paper.
II. OPERATIONAL PRINCIPLE
The circuit of the proposed converter is based
on the conventional buck converter. However, to
achieve the
ZCS’operation
of the power switch during the turn-on transition, the
proposed converter uses an additional switch Sx, a
diode Dx, and a coupled winding on the original buck
inductor, as shown in Fig. 1. The coupled winding has
to have a fewer number of turns compared with the
buck winding for proper operation. Since the rms
current of the coupled winding is small, the coupled
winding requires a small winding area. Therefore, the
buck core of the proposed converter is not much
larger than that of the conventional one. In addition,
the transferred coupled-winding current is not
circulating but delivering to output. Therefore, the
proposed converter does not greatly increase the
magnetics size of the conventional buck inductor. The
current stresses of the additional switch Sx and diode
Dx are also small. The basic idea of the proposed
converter is simple. When switch Sx is turned on, the
buck-inductor current is transferred to the coupled
winding for a short period. Then, after the leakage
inductor current iLr reaches zero, it flows in the
negative direction and to the discharge output
capacitor of switch Sb for ZCS operation.
Fig. 2 shows the gating pulses for the
switches and key operation waveforms of the
proposed converter in a steady state. One switching
cycle is divided into six modes, and their operational
stages are shown in Fig. 3. In order to simplify the
analysis of the steady-state operation, all parasitic
components except for those specified in Fig. 1 are
neglected. It is assumed that the output capacitors of
switch Sb and diode Db have the same capacitance as
Cs for simple analysis. Moreover, the output current
Io, output voltage Vo, and input voltage Vin are
assumed constant during the switching cycle.
Mode 1 (t0−t1): Mode 1 begins when the
leakage inductor current iLr reaches the buck-inductor
current iLb. Since buck switch Sb is in the ON state,
the difference between the input voltage Vin and
output voltage Vo is applied in buck inductor Lb with
the assumption of Lb _ Lr. Then, the coupled-winding
voltage vx, switch voltage vSx, and diode voltage vDx
are
given by
vx =n(Vin − Vo) (1)
vSx =(1 − n)(Vin − Vo) (2)
vDx =Vo + n(Vin − Vo). (3)
Since n < 1 and Vin > Vo, vSx and vDx are positive
and the
output diode of switch Sx and diode Dx remains in the
OFF
state. This mode ends when switch Sb is turned off.
Mode 2 (t1−t2): In Mode 2, diode Db is in the
ON state and the buck-inductor voltage vLb = −Vo.
Then, the coupled winding voltage vx, switch voltage
vSx, and diode voltage vDx are given by
vx = − nVo (4)
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vSx =Vin − (1 − n)Vo (5)
vDx =(1 − n)Vo. (6)
Since n < 1 and Vin > Vo, vSx and vDx are positive
and the
output diode of switch Sx and diode Dx remain in the
OFF state. This mode ends when switch Sx is turned
on.
Mode 3 (t2−t3): When switch Sx is turned on, the
coupled winding voltage vx becomes Vin − Vo. Then,
the voltages of the buck and leakage inductors are
given by
vLb =(Vin − Vo)/n (7)
vLr = −1/n (Vin − Vo) + Vo) (8)
Since turn ratio n is a little smaller than unity, the
leakage
inductor voltage vLr has a large negative value, as
shown in
Then, the leakage inductor current iLr decreases
rapidly down to zero since leakage inductor Lr has a
very small value. The time interval of this mode is
short and given by
The current difference between iLb and iLr is
transferred
to the coupled winding and flows through switch Sx
and the
output capacitor, as shown in Fig. 3(c). As shown by
the
powering current iPo in Fig. 2, the transferred current
is not
circulating but powering to output. Therefore, it does
not induce additional conduction losses and does not
increase the dc value of the buck-inductor current.
The voltage of diode Dx and switch Sb is Vin during
this mode. This mode ends when the leakage inductor
current iLr reaches zero.
Mode 4 (t3−t4): Mode 4 has a very short period
compared
with Mode 3. After the leakage inductor current iLr
reaches
zero, it flows in the negative direction and to the
charge and discharge output capacitors of diode Db
and the output capacitor of switch Sb, respectively, in
a resonant manner, as shown in Fig. 3(d). Since switch
Sx is still in the ON state, the voltage across the buck
inductor remains the same as . Then, the voltage of the
leakage inductor is given by
where vSb(t) varies from Vin to the zero current
during this
mode. The voltage of switch vSb(t) and the current of
the
leakage inductor iLr(t) is given by
As shown in the leakage inductor voltage vLr in Fig. 2
and
the voltage across the leakage inductor is always
negative during t3−t4, which forces the leakage
inductor current to flow in the negative direction
always in this mode. Therefore, the ZCS of switch Sb
is always achieved only if switch Sx is not turned off
before vSb reaches zero. Since the leakage inductor Lr
and output capacitor Cs have very small values and
the time interval within which switch voltage vSb
decreases from Vin to zero is very short, switch Sx is
never turned off before vSb reaches zero. Therefore,
the ZCS of switch Sb is already guaranteed. The time
period of this mode is given by
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Mode 5 (t4−t5): Mode 5 begins when vSb(t) reaches
zero
current. When vSb reaches zero current, the output
diode of
switch Sb is turned on and maintained in its ON state
during
this mode since the voltage of the leakage inductor
vLr remains
negative as
which maintains that the leakage inductor current still
flows in the negative direction, as shown in Fig. 3(e).
Since turn ratio n is slightly less than unity in the
voltage across the leakage inductor vLr has a very
small negative value and the leakage inductor current
iLr increases negatively in a very slow slope, as
shown by the leakage inductor current iLr in Fig. 2
during t4−t5. Therefore, the unnecessary current
increment after the zero current of the power switch is
achieved is minimized. This is an advantage of the
proposed converter since, from full to light-load
conditions, the proposed converter minimizes the
unnecessary current increment after the zero current
of switch Sb is achieved. This will be explained more
detail in the next section. Mode 5 ends when switch
Sx is turned off.
Mode 6 (t5−t0): As switch Sx is turned off, switch Sb
is
turned on with a zero-current condition since the
output diode of switch Sb is in the ON state. The
transferred current to the coupled winding flows
through diode Dx. The voltage across buck inductor
vLb and leakage inductor vLr is given by As shown in
since a large positive voltage is applied to the leakage
inductor, the leakage inductor current iLr increases
rapidly up to the buck-inductor current iLb(t0). This
mode ends when the leakage inductor current iLr
reaches the buck-inductor current iLb.
III. ANALYSIS OF THE PROPOSED
CONVERTER A. ZCS Criteria
Since the ZCS operation of the proposed converter
is always guaranteed only if the duty of Sx is larger
than the interval of t4−t2, the ZCS criterion of the
proposed converter is given by
which is based . Since the time interval t4−t2 has its
largest value at maximum load current Io.max, this
condition should be determined in full-load
conditions.
B. Operation With Load Variation
In the proposed converter, the ZCS operation of
buck switch Sb is achieved by a negative leakage
inductor current iLr. The negative leakage inductor
current increases in very small values after switch
voltage vSb reaches zero current. Therefore, the
unnecessary current increment in negative value is
minimized in light-load conditions and conduction
loss is also minimized. Fig. 4 shows the operation of
the proposed converter in full- and light-load
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conditions, respectively. Since the negative current
flow of iLr makes the ZCS operation of the power
switch possible, the ZCS operation of switch Sb can
be explained with the leakage inductor current iLr
during t3−t5. In this figure, the switch voltage vSb
reaches zero current at time t4. As shown
in Fig. 4(a), since the buck-inductor current iLb has a
large
value at the full-load conditions, the time interval of
t2−t3 is
long. Moreover, during time interval t3−t4, the zero
current of switch Sb is achieved by resonance. After
the voltage of switch Sb reaches zero, the output diode
of Sb remains in the ON state until switch Sb is turned
on with the zero-current condition. Since the duty of
switch Sx is determined by in full-load conditions, the
time interval of t4−t5 can be set as small as possible
in full-load conditions.
Fig. 4(b) shows the operation of the proposed
converter in
light-load conditions. In the practical case, the duty of
switch Sb decreases as the load goes down due to the
parasitics in the circuit. However, the duty of switch
Sx is fixed as the design criteria of and does not
change as the load goes down. Since the buck-
inductor current iLb is small in lightload conditions,
the time interval of t2−t3 is short. Moreover, during
time interval t3−t4, the zero current of switch Sb in
light load condition is achieved in the same resonance
manner as that of full-load conditions. However, since
time interval t2−t3 is short, time interval t4−t5 is long
in light-load conditions. As shown in the vLr
waveform in Fig. 4(b), the leakage inductor voltage
vLr has a very small value as (14) during time interval
t4−t5. Therefore, the leakage current only increases
very slightly in the negative direction even though
time interval t4−t5 is long. Finally, the value of the
negatively increased leakage inductor current in light-
load conditions is very small and a little larger than
that in full-load conditions. Therefore, the
unnecessary current increment is minimized in light-
load conditions after the zero current of switch Sb is
achieved, and the conduction loss is also minimized.
C. DC Characteristics of the Proposed Converter
Applying current-second balance in the buck inductor,
the
voltage conversion ratio of the proposed converter is
given by
Since the duty ratio Da is function of the load current,
the
voltage conversion ratio of the proposed conversion
changes with the load variation. However, its effect is
very small and the voltage conversion ratio
approaches D when Dx and Da are assumed to be very
small.
As shown by the powering current ipo in Fig. 2, the
powering current which is slightly higher than the
buck-inductor current iLb is delivered to the output.
Therefore, there is no increment in the buck-inductor
dc compared with the conventional one and the dc
value of the buck-inductor current ILb.DC is very
slightly lower than Io and can be assumed to be Io,
considering the short period of (Dx + Da)Ts and the
small increment of the powering current during this
period.
IV. DESIGN AND EXPERIMENTAL RESULTS
To validate the characteristics of the proposed
converter, the prototype converter is designed and
tested with the following specifications:
1) input voltage Vin = 100 V dc;
2) output voltage Vo = 50 V;
3) maximum output power Po.(max) = 300 W;
4) switching frequency fs = 125 kHz.
Based on (17), the minimum duty ratio of switch Sx
can be determined according to the leakage inductor
value Lr and coupled-winding turn ratio n, as shown
National Conference on Research Advances in Communication, Computation, Electrical Science and Structures
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in Fig.
The components used in the prototype of the proposed
converter are IRFP250N for switch Sb, MUR2020CT
for
diode Db, IRF640 for switch Sx, MUR2020CT for
diode Dx,
4.2 μH for leakage inductor Lr, 250.66 μH for buck
inductor
Lb, and 0.85 for the coupled-winding turn ratio n. An
EER
4042 ferrite core for buck inductor Lb and a current
density
of 600 A/cm2 for the wire are used. Based on Fig. 5,
the
minimum duty ratio of switch Sx is obtained 0.038,
and 0.05
is used in the experiment for the prototype of the
proposed
converter. Fig. 6 shows the key experimental
waveforms of
the proposed converter in full-load conditions. The
leakage
inductor current iLr is rapidly decreasing and
transferring to the coupled winding during switch Sx’s
on period, and the coupled winding current ix is
rapidly increasing at the same time. The current of
switch Sb has a small negative value at the turn-on
switching moment, and ZCS is observed in the switch
voltage vSb. When the leakage inductor current iLr
increases to the buck-inductor current iLb, the
resonance between the leakage inductor Lr and the
output capacitors of the auxiliary switch and diode
occurs. However, in Fig. 6, the waveforms of vSx and
vDx are not full sinusoidal waveforms even though
they are resonant. This is because vSx and vDx are
clamped to input voltage Vin. Therefore, when one of
vSx or vDx increases to input voltage Vin, the voltage
is clamped to Vin. Fig. 7 shows the ZCS operation of
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switch Sb with load variations. In all load conditions,
the ZCS operation is achieved. In addition, in each
load condition, the switch current does not rapidly
increase in the negative direction and has a very small
negative slope after the zero current of switch current
iSb is achieved. This prevents unnecessary current
increase in the negative direction and minimizes the
conduction loss of the switch and inductor windings.
Fig. 8 shows the zero-current turn-on switching (ZCS)
of switch Sx in the switching moment. Fig. 9 shows
the efficiency comparison of the proposed converter
with the conventional buck converter with load
variations. The proposed converter shows around 95%
efficiency from half- to full-load
conditions,which is around 1.5% higher efficiency
than that of the conventional buck converter. Fig. 10
shows the comparison of the loss distributions of the
proposed and conventional buck converters in full-
load conditions. The proposed converter induces more
losses in the inductor and additional loss in the
auxiliary switch and diode. However, the proposed
converter achieves loss reduction in the buck switch
by soft switching and in the buck diode through the
reduced diode current. Finally, the proposed converter
achieves loss reduction compared with the
conventional buck converter.
Fig. 11 shows the boost counterpart of the proposed
concept. The operation of the boost converter of the
proposed concept is a lot similar to that of the buck
converter. The only difference is that it is based on the
boost converter operation instead of the buck
converter operation.
V. CONCLUSION
This paper has presented the operational
principle, analysis, design example, and experimental
results of the buck converter with a coupled winding,
showing the excellent ZCS operation of the power
switch from heavy- to light-load conditions. Since the
buck-inductor current is transferred to the coupled
winding in a very short period when the auxiliary
switch is in the ON state, the negatively built-up
leakage inductor current of the buck winding
guarantees the ZCS operation of the buck switch in all
load conditions. Furthermore, since the negatively
built up leakage inductor current is minimized after
the zero current
of the buck switch is achieved, the unnecessary
current buildup is minimized and the conduction loss
is also minimized as the load goes down. The basic
operational principle has been presented in the mode
analysis and key characteristics, such as those for the
ZCS operation, and the dc values have also been
presented in the analysis. Based on the design
equations, a prototype converter has been built and
tested. The experimental results of the 300-W
prototype converter prove the key characteristics of
the proposed converter. Around 95% efficiency was
observed from half- to full-load conditions.
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National Conference on Research Advances in Communication, Computation, Electrical Science and
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Efficient Battery Storage Application for Residential Load
Connected PV System
A.P.Suganya1, Mrs. C.Santhana Lakshmi, M.E, (Ph. D)
2, Dr. C. Nagarajan,B.E, M.Tech, Ph.D
3
1PG Scholar Department of EEE, Sona College of Technology, Salem-5
2Assistant Professor, Department of EEE, Sona College of Technology, Salem-5
3Professor, Department of EEE,Muthyammal Engineering College, Rasipuram
Abstract: This project presents a technique to
control power flowand battery storage for load
connected PV systems in the residential level in
order to optimize the use of PV energy. The
battery is used to supply energy with two
conditions that it should not over charge and it
should not over discharge for the reason of saving
battery life. Here, the battery should be operation
is based on proposed modes and conditions
without any loss.The objective is to increase the
use of battery in order to avoid electricity
purchase from gird during load demand. Because
the use of PV system is increased due to its high
availability. A type of converter is used to
maintain the constant voltage and current. A grid
connected PV system has been arranged to
validate the proposed strategies.A simulation
system was established on the
MATLAB/SIMULINK.
Keywords: Photovoltaic system, buck boost,
converter, SOC algorithm, Battery modes.
1. Introduction
The evolution of fossil fuels reserves and the
ever rising environmental pollution have increased
strongly during last decades the development of
renewable energy sources (RES). The need of having
available continuous energy systems for replacing
gradually conventional ones demands the
improvement of structures of energy supply based
mostly on clean and renewable resources.
Nowadays,photovoltaic (PV) generation is assuming
increased importance as a RES application because of
distinctive advantages such as simplicity of
arrangements, high dependability, no fuel cost, low
maintenance, lack of noise and wear due to the
absence of moving parts. Also the solar energy gives
a clean, pollution free and natural energy source. In
addition to these factors are the limited cost of solar
modules, an increasing efficiency of solar cells,
manufacturing technology improvements and
economies of scale [2].
The increasing range of renewable energy
sources and distributed generators needs new
methods for the operation and management of the
electricity grid so asto keep upor to enhance the
power-supply responsibility and quality.
Additionally, relaxation of the grids ends up in new
management structures, during whichcommercialism
of energy and power is turning intomore and
morevital. The specific application (whether it's off-
grid or grid-connected), battery storage size is
decidedsupported the battery specifications for
optimum charging and discharging rate (units of kW)
and therefore the battery storage capability (units of
kWh). For off-grid applications, batteries ought to
fulfill the subsequent requirements: (i) the
discharging rate should be greater or equal than the
height load capability; (ii) the battery storage
capacity should be large enough to providethe most
importantgetting dark energy use and to be ready to
supply energy throughout the longest cloudy amount.
The IEEE normal provides filler recommendations
for lead-acid batteries in complete PV systems. The
solar array size and therefore the battery size
arechosen via simulations to optimize the operation
of a complete PV system, that considers
responsibility measures in terms of loss of load hours,
the energy loss and therefore the total value. In
distinction, if the PV system is grid-connected,
autonomy could be a secondary goal; instead,
batteries willscale back the fluctuation of PV output
or offer economic benefits like demand charge
reduction and capability firming.
The standalone systems are highly built on
residential households with battery storage. Even at
night and stormy weather conditions, it will able to
provide the energy to the load. A charge controller is
used in the system to prevent overcharging and deep
discharge of the batteries called state of charging.
These systems generally include an inverter, which
converts the DC voltage of PV modules into AC
voltage for direct use with the appliances.At present
grid-connected photovoltaic (PV) systems are
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recognized for their contribution to clean power
production.
2. PV system configuration
The simple arrangement of grid-connected PV system configuration is shown in Fig.l. It consists of twopower converters: a DC-DC and a DC-AC converter. Both are bi-directional power converters.
Fig.1 Simple PV system
1. Modelling of PV cells
A photovoltaic (PV) system directly
converts sunlight into electricity. The basic device of
a PV system is the photovoltaic (PV) cell. The
photovoltaic module is the result of associating a
group of PV cells in series and parallel and it
represents the conversion unit in this generation
system. An array is the result of associating a group
of photovoltaic modules in series and parallel. The
obtained energy depends on solar radiation, the
temperature of the cell and the voltage produced in
the photovoltaic module. The voltage and current
available at the terminals of a PV device may
directly feed small.
Fig.2 Equivalent circuit diagram of PV cell
The relationship between the PV cell output
current and terminal voltage according to the single-
diode model is governed by equation (1), (2) and (3).
I = Iph – ID (1)
ID = I0 (2)
I= Iph - I0 (3)
The basic equation (3) of the elementary PV
cell does not represent the I-V characteristic of
practical ones. Practical modules are composed of
several connected PV cells which requires the
inclusion of additional parameters, Rs and Rp, with
these parameters equation (3) becomes equation (4).
I= Iph – I0 (4)
Where Iphis the current generated by the incident
light, ID is the diode current, I0 is the reverse
saturation current, q is the electron charge, k is the
Boltzmann constant, α is the ideality factor. T is the
temperature,.Rs is the series resistance, RP is the
parallel resistance.
2. Modelling of PV array
The number of cells are connected to form a
PV array. The equivalent circuit of PV array are
shown in fig.2
Fig.3 Equivalent circuit diagram of PV array
The current-voltage relationship of PV array is given
by equation (5).
IA = NpIph - NPIRS
+ ∗ (5)
3. DC-AC Converter Power Circuit
The DC-AC converter connected between
DC and AC bus to function either as the inverter to
National Conference on Research Advances in Communication, Computation, Electrical Science and
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give power from DC side to the AC side (grid) or as
the PWM rectifier used to recharge the battery from
grid power in particular occasions. The DC-AC
power circuit is constructed from the full bridge
topology as shown in Fig.4
Fig.4 DC-AC power circuit
The converter output is connected to the grid via a
filter inductor and a transformer. A capacitor is
connected with an inductor to form a LC filter for
filtering output voltage in standalone mode.
However, this capacitance should be chosen to be as
small as possible to allow the control system to be
closely real time control. Furthermore, with the
small filter capacitance, the current control loop can
be designed as a simplified L-filter inverter. This
can avoid the instability problem in the system.
4. DC - DC Converter Power Circuit
The power from DC-DC converter bus to
recharge the batteries or to feed the stored energy in
battery to DC bus.Therefore, the power topology of
DC-DC converter is a bidirectional DC chopper is
shown below in fig.5
Fig.5DC-DC converter power circuit
The converter can operate either in buck or
boost mode depending on desired current in battery
(Ibatt) direction. In buck mode, the power from the
DC bus is used to charge the battery, whereas in
boost mode,battery releases the absorbed energy to
DC bus.
III. Battery Modes and Algorithm
The battery should be operated during the
load demand ,but the charging and discharing of
battery should be noted and it based on the SOC
algorithm. The load demand should not be occurred
to which the battery is cut off during the panel
output satisfies the demand if not battery also
combined to satisfies the demand.
A battery is made of one or more cells, it
was either parallel or series connected to obtain a
required current/voltage capability. The voltage of a
battery when at rest (not supplying current) will vary
according to how fully charged the battery.
1. Battery Charging
The rate of charge or current will flow that depend on
the difference between the battery voltage and the
voltage that is inject to battery from solar panel.
While it is beneficial to a battery performance and
life to be fully charged on regular occasions, however
once a battery has been charged to its full capacity, it
is important not to continue charging as this will
damage the battery. A Charge Controller is necessary
to ensure that the battery is not over charged.
Fig.6 Battery charging and discharging
2. Battery discharging
As the battery approaches the fully discharged state,
the voltage starts to fall more quickly again. It is
important for a battery to never be fully discharged,
so your inverter will normally disconnect at 90%
volts. An interesting point to note here is that when
an inverter or other power load is drawing a high
current from the battery, the voltage will drop. This
may mean that the battery needs to be somewhere
over 50% charged to avoid the inverter cutting out
due to low voltage.
3.Battery model
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The most commonly used battery model is
shown in figure it made of an ideal battery with open
circuitvoltage Vo, a constant equivalent internal
resistance Rint and the terminal voltage Vt.
Fig.7 Simplified battery model
The terminal voltage can be obtain from the
open circuit measurement and Rint can be measured
by connecting a load and measuring both terminal
voltage and current, at fully charged conduction this
model does not take into consideration of varying
nature of the internal resistance due to temperature,
state of charge and electrolytic concentration this
kind of model can be used for certain circuit
simulation and not be used for high voltage
applications.
4. Modes and conditions
Mode 1: load demand can only meet by PV. Mode 2:
load demand can be meet by PV and battery.
Condition 1: Battery should be charged upto 90%
during low load. Condition 2: Battery should be
discharged upto 20% during load demand cannot be
meet by PV panel.
5. Battery based on SOC algorithm
To efficiently run the load, the battery
should be charged upto the valid limits. So a need
for measuring the charge of the battery commonly
known as state of charge(SOC). But,a control unit to
be aware of the battery capacity accurately and help
to maintain the state of charge within in the safe
limits. Measuring the battery state of charge (SOC)
is not an easy task because the depends on
temperature, battery capacitance and battery internal
resistance. The charging and discharging of the
battery is constantly taking place, therefore keeping
track of the SOC is an essential butdifficult task.
But, the main objective of the project is to
maintain the state of charge of the battery accurately
and effectively. The approach is done by use of a
linear relationship between Voc and SOCis given by
the equation (6) and (7)
Voc(t) = aS(t) + b (6)
S(t) = [Voc(t) - b] / a (7)
Where s(t) is state of charge ,Voc(t) is the open
circuit voltage ,b is the terminal voltage of battery.
When S(t) = 0% and a is obtained by defining the
value of b and Voc at S(t) 100%. Based on equation
(1),there is a great interest in using open circuit
voltage to determine the SOC of the battery has been
disconnected from the load, which is not possible
during the load demand. To overcome this difficulty,
where the parameter Voc is estimate ever under load
condition. Thereby, we can identify open circuit
voltage and subsequently the SOC of the battery.
The optimization toolbox from MATLAB operates
on measured values current and voltage from the
battery, to find an optimumvalue for the battery, to
find an optimum value for the battery parameters in
order to minimize the error between the calculated
terminal voltage and calculated voltage of the
battery.
1V.SIMULATION RESULTS
The simulation result of the residential load
connected battery storage application. The output
(voltage and current) from the PV panel is shown in
fig (8) & (9).
Fig.8 output voltage from PV panel
National Conference on Research Advances in Communication, Computation, Electrical Science and
Structures (NCRACCESS-2015)
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Fig 9 output current from the PV system
Fig.10 charging and discharging of the battery
Charging( 90%) and discharging (20%)
Fig.11 converted single phase output for the
residential load
V. CONCLUSION AND DISCUSSION
The guideline to control the power flow
control parameter to achieve the operation for
residential load connected PV system with battery
storage are presented. Here, the excess electricity
generated from the PV system is stored in battery and
electricity must be purchased from the grid if meet
cannot be meet. For latter research the objective is to
reduce the cost by purchasing electricity from grid
and the capacity loss by linear programming and
techno-economical optimization techniques. This will
lead initiated idea for the future research with PV
system.
References
[1]. Power Flow Control and MPPT Parameter
Selection for Residential Grid-Connected PV
Systems with Battery Storage,Chuenwattanapraniti
Chokchai,The 2014 International Power Electronics
Conference.
[2]. Om prakash mahela and sheesh rom ola
,modeling and control of grid connected photo voltaic
system: A review ,IJEEER,Vol. 3, Issue 1, Mar 2013,
123-134.
[3]. Battery Sizing for Grid Connected PV Systems
with Fixed Minimum Charging/Discharging Time.Yu
Ru, Jan Kleissl, and Sonia Martinez are with the
Mechanical and Aerospace Engineering Department,
University of California, San Diego (e-mail:
[email protected],[email protected],[email protected]
du).
[4]. Modeling and Control of Grid Connected
Photovoltaic System- A Review,IJATAE,ISSN 2250-
2459, ISO 9001:2008 Certified Journal, Volume 3,
Issue 3, March 2013.
[5]. Optimal Sizing of Combined PV- Energy Storage
for a Grid-connected Residential Building.Ghassem
Mokhtari*, Ghavam Nourbakhsh*, Arindam
Gosh*,Advances in Energy Engineering (AEE)
Volume 1 Issue 3, July 2013.
[6]. State of charge estimation for batteries, A thesis
for Master of science degree,The university of
Tennessee,Knoxville,December,2002.