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Power Quality Improvement in Distribution Systems

Using Fuzzy Based Hybrid Active Power Filter

Somlal Jarupula

#1, Dr.Venu Gopala Rao.Mannam

#2, Ramesh Matta

#3

#1Associate Professor, Department of EEE, K.L.University, Vijayawada, A.P, India-522502

#2Professor, Department of EEE, K.L.University, Vijayawada, A.P, India-522502

#3M.Tech., student, Department of EEE, K.L.University, Vijayawada, A.P, India-522502

-----------------------------------------------------------------------------------------------ABSTRACT

In this paper, a fuzzy based hybrid active power filter (FHAPF) is proposed for reducing

harmonics and improving the power factor of a distribution system. The proposed filter

consists of two control circuits such as Generalized PI control unit and fuzzy adjustor unit, in

which, generalized PI control unit is used for achieving dividing frequency control and the

fuzzy adjustor unit is used for adjusting parameters of the PI control unit to produce better

adaptive ability and dynamic response. This method is proposed to minimize the capacity of

FHAPF by analyzing the bode diagram with two control units such as 1) generalized

integrator control unit is used for dividing frequency integral control and 2) fuzzy adjustor

unit is used for adjusting proportional-integral coefficients timely. The proposed control

method is generally applicable for any type of other active filters. Simulations are carried out

by using MATLAB, it is observed that the power factor has been improved to 0.985 and the

%THD is reduced to 0.78 by the proposed technique. The simulation and experimental results

also show that the new control method is not only easy to be calculated and implemented, but

also very effective in reducing harmonics.

Key words: Distribution System, Fuzzy Adjustor Unit, Fuzzy Dividing Frequency-Control,

Generalized PI Control Unit, FHAPF, Power Quality.

Corresponding Author: Somlal Jarupula

INTRODUCTION

In the distribution system, Harmonics are the major concerned problem. The growing use of

electronic equipments is one of the major causes to impute the harmonics. In order to solve

these problems, the passive power filter (PPF) is often used conventionally. However, it has

many de-merits such as being bulk, resonance, tuning problem, fixed compensation, noise,

increased losses, etc., which discourages its implementation [1]–[3]. On the contrary, the

APF can solve the above problems and is often used to compensate current harmonics and

low power factor that is caused by nonlinear loads [4]-[5]. The performance of active filter

depends on the adoptive control approaches. There are two major parts of an active power

filter controller: a) To generate APF reference voltage vector instead of reference current; b)

to generate desired APF output voltage by Space Vector Pulse Width Modulation (SVPWM)

based on generated reference voltage [6]. The HAPF is the combination of active and passive

power filters [7]. HAPF is categorized in parallel hybrid active power filters (PHAPFs) and

series hybrid active power filters (SHAPFs) based on the used active filter type. A series of

PHAPFs was proposed after the 1990s [8]–[9]. Cheng et al. proposed a new hybrid active

power filter to achieve the power-rating reduction of the active filter [10]. But the active this

paper, a novel HAPF with injection circuit was proposed.

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The proposed method has great promise in reducing harmonics with a relatively low capacity

APF. For harmonic current tracking controls, there are two schemes [11]–[14]:One is the

linear current control, such as ramp comparison control, deadbeat control, sinusoidal internal

model control, generalized integrators control, etc.; the other is nonlinear current control,

such as hysteresis control, predictive control, etc. Hysteresis control has the advantage of

simplicity, but leads to a widely varying switching frequency. This limitation has been

improved with variable hysteresis band switching strategies but it requires a complex

controller to achieve satisfactory performance. Predictive current control offers the best

potential for precise current control, but the implementation of a practical system can be

difficult and complex. In this paper, an adaptive fuzzy dividing frequency-control (AFDFC)

method was proposed, which is composed of a generalized PI control unit and fuzzy adjustor

unit. The simulation and experimental results also show that the new control method is not

only easy to be calculated and implemented, but also very effective in reducing harmonics.

CONFIGURATION OF THE SYSTEM

Fig.1 shows a PHAPF that is in use now [8]. It consists of the 3-phase source, universal

bridge, load along with both active and passive filters. The parallel HAPF has the advantages

of easy installation and maintenance and can also be made just by transformation on the PPF

installed in the grid. A Passive Power Filter (PPF) has been designed for some certain orders

of harmonics in order to reduce the power of APFs. It is connected in shunt to the system

whose control is given from reference current calculator and generalized integral controller

for pulse generation. This filter consists of bridge circuit of IGBT, across which a diode is

placed. The IGBT acts accordingly to the pulses and it charge through the diode and

discharge through the capacitor, in this an impedance circuit is connected in series to reduce

the ripples. The main purpose of this filter is that it acts accordingly to the dynamics of the

system and injects a waveform which is in opposite polarity to the harmonic waveform

thereby cancelling it.

Fig.1 Topology of the shunt hybrid APF

The lower and higher order harmonics are reduced by the passive filter and the other order

harmonics are reduced by the active filter. In Fig.1, let C2, L2; C5, L5 and C7 and L7 are the

components from left to right respectively to make up a PPF to compensate the second, fifth,

and seventh harmonic current, while the APF is just used to improve the performance of PPF

and get rid of the resonance that may occur. So the power of the filter can be reduced sharply,

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usually one-tenth of the power of the nonlinear load, which enables the APF to be used in a

high-power occasion.

Fig.2 Topology of the FHAPF

Reactive power as well as damp harmonics in the distribution system can expected to

compensate by HAPF and all of the reactive power current will go through APF. To further

decrease the power of APF, a novel configuration of the hybrid APF is proposed as shown in

Fig.2. L1 and C1 tune at the fundamental frequency, and then compose the injection branch

with CF.

CONTROL STRATEGY OF FHAPF: FUZZY ADJUSTOR UNIT METHOD

The dynamic response of the system and/or to increase the stability margin of the closed loop

system, the conventional linear feedback controller (PI controller, state feedback control, etc.)

can be utilized. However, these controllers may present a poor steady-state error for the

harmonic reference signal. An AFDFC method is presented in Fig.3, which consists of two

control units: 1) a generalized integrator control unit, which can ignore the influence of

magnitude and phase, is used for dividing frequency integral control and 2) a fuzzy adjustor

unit or fuzzy arithmetic is used to timely adjust the PI coefficients.

Since the purpose of the control scheme is to receive a minimum steady-state error, the

harmonic reference signal r is set to zero. First, supply harmonic current is detected. Then,

the expectation control signal of the inverter is revealed by the fuzzy dividing frequency

controller. The stability of the system is achieved by a proportional controller, and the perfect

dynamic state is received by the generalized integral controller. The fuzzy adjustor is set to

adjust the parameters of proportional control and generalized integral control. Therefore, the

proposed harmonic current tracking controller can decrease the tracking error of the harmonic

compensation current, and have better dynamic response and robustness.

A. Fuzzy Adjustor

The fuzzy adjustor is used to adjust the parameters of proportional control gain K P* and

integral control gain KI based on the error e and the change of error K P= K P

*+ K P (1)

K I= K I*+ K I (2)

Where K P* and KI

* are reference values of the fuzzy-generalized integrator PI controller. In

this paper, K P* and KI

* are calculated offline based on the Ziegler–Nichols method. A block

diagram of fuzzy-logic adjustor is shown in Fig.4.

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Fig.3 Configuration of the fuzzy dividing frequency controller

Fig.4 Block diagram of the fuzzy adjustor unit

The error e and change of error ec are used as numerical variables from the real system. To

convert these numerical variables into linguistic variables, the following seven fuzzy levels or

sets are chosen as [17]: negative big (NB), negative medium (NM), negative small (NS), zero

(ZE), and positive small (PS), positive medium (PM), and positive big (PB). To ensure the

sensitivity and robustness of the controller, the membership function of the fuzzy sets for

e(k), (k), K p and K p in this paper are acquired from the ranges of e, K p and K I,

which are obtained from project and experience and the membership functions are shown in

Fig.5, respectively.

The core of fuzzy control is the fuzzy control rule, which is obtained mainly from the

intuitive feeling for and experience of the process. The fuzzy control rule design involves

defining rules that relate the input variables to the output model properties. For designing the

control rule base for tuning K p and K I, the following important factors have been taken

into account.

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Fig.5. Membership functions of the fuzzy variable. (a) Membership function of e (k) and ec(k) (b) Membership

function of K p and K I

1) For large values of /e/, a large K p is required, and for small values of /e/, a small K p is

required.

2) For e. , a large K p is required, and for e. a small K p is required.

3) For large values of /e/ and / , K p is set to zero, which can avoid control saturation.

4) For small values of /e/ , K p is effective, and K p is larger when /e/ is smaller, which is

better to decrease the steady-state error. So the tuning rules of K p and K I can be obtained

as Tables1, 2. TABLE 1

ADJUSTING RULE OF THE K p PARAMETER

K p

NB NM NS 0 PS PM PB

e

NB PB PB NB PM PS PS 0

NM PB PB NM PM 0 0 0

NS PM PM NS PS NS NS NM

0 PM PS 0 0 NS NM NM

PS PS PS 0 NS NM NM NM

PM 0 0 NS NM NM NM NB

PB 0 NS NS NM NM NB NB

TABLE 2 ADJUSTING RULE OF THE K I PARAMETER

K I

NB NM NS 0 PS PM PB

e

NB 0 0 NB NM NM 0 0

NM 0 0 NM NM NS 0 0

NS 0 0 NS NS 0 0 0

0 0 0 NS NM PS 0 0

PS 0 0 0 PS PS 0 0

PM 0 0 PS PM PM 0 0

PB 0 0 NS PB PB 0 0

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The inference method employs the MAX-MIN method. The imprecise fuzzy control action

generated from the inference must be transformed to a precise control action in real

applications. The center of gravity method is used to defuzzify the fuzzy variable into

physical domain.

RESULTS AND DISCUSSIONS

Simulation results of a 15-kV system have been carried out with MATLAB/SIMULINK

software. The system parameters are listed in Table.3. The PPFs are turned at the 11th and

13th, respectively.

Table 3

Parameters of the FHAPF

L/mH C/ F Q

Output filter 0.2 60

11th turned filter 1.77 49.75 50

13th turned filter 1.37 44.76 50

6th turned filter 14.75 CF:19.65,CI:690

The injection circuit is turned at the 6th. In this simulation, ideal harmonic current sources are

applied. The dc-side voltage is 535 V. Simulation results with the conventional PI controller

and the proposed current controller are shown in Figs. IL,Is,IF,Iapf and the error represent the

load current, supply current, current though the injection capacitor, current through APF, and

error of compensation.

Fig.6. Simulation circuit of a FHAPF

Fig.6 shows the simulation circuit of a FHAPF. Fig.7 shows the simulation results of the

dynamic performance with the conventional PI controller. Fig.8 shows the simulation results

of the dynamic performance with the conventional generalized integral controller. Fig.9

simulation results of the dynamic performance with the proposed controller. It is observed

from Fig.10 and Fig.11 that at 0.2s to 0.3 s, the THD increases from 9.40% to 21.34%. When

the conventional PI controller is used, the error can be reduced to ±09A in 0.05 s, but there is

an obvious steady state error at 1.0 s all the same. When the generalized integral controller is

used, the error reduces to ±06 A at 0.5 s; however, it can only be reduced to ±15 A in 0.05 s.

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When the proposed controller is used, the error can be reduced to 1.5 A in 0.05 s. It is

observed that compared to the conventional PI controller and generalized integral controller,

the proposed controller has better dynamic performance. Fig.12 shows the steady-state

performance of the FHAPF when different controllers are used.

Fig.7 Simulation results of the dynamic performance with the conventional PI controller

Fig.8 Simulation results of the dynamic performance with the conventional generalized integral controller.

Fig.9 Simulation results of the dynamic performance with the proposed controller

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Fig.10 FFT analysis at 0.2sec for FHAPF

Fig.11 FFT analysis at 0.3sec for FHAPF

Fig.12 FHAPF steadystate

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Fig.13 Simulation results of steady-state compensation with the conventional PI controller

Fig.14 Simulation results of steady-state compensation with the conventional generalized integral controller

Fig.15 Simulation results of steady-state compensation with the proposed controller

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Fig.16 FFT analysis for the FHAPF with the proposed controller

Fig.17 Power factor with the proposed controller

Fig.13 shows the Simulation results of steady-state compensation with the conventional PI

controller. Fig.14 shows the simulation results of the steady-state compensation with the

conventional generalized integral controller. Fig.15 shows the simulation results of the

steady-state compensation with the proposed controller. From Fig.13, it can be seen that after

FHAPF with the conventional PI controller runs, the current total harmonic distortion reduces

to 3.42% from 21.34%, and the power factor increases to 0.985 from 0.6 as shown in Fig.17.

When the conventional generalized integral controller is used, the current THD reduces to

3.42% from 21.34%, while after the FHAPF with the proposed PI controller runs; the current

THD reduces to 0.78% from 21.34% as shown in Fig.16. So it can be observed that the

proposed current controller exhibits much better performance than the conventional PI

controller and the conventional generalized integral controller.

Table 4

Comparison of supply current %THD and power factor

THD Power Factor

Without FHAPF 21.34% 0.6

With FHAPF 0.78% 0.985

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CONCLUSION

A fuzzy based hybrid active power filter (FHAPF) was proposed. Generalized PI control unit

and fuzzy adjustor unit based fuzzy dividing frequency-control method (FDFC) method was

discussed clearly. The proposed method is able to increase the response of the dynamic

system, robustness and also which is able to decrease the tracking error. The proposed

method is very much useful and also applicable to any other type of active filters. Simulations

are carried out by using MATLAB, it is observed that the power factor has been improved to

0.985 and the %THD is reduced to 0.78 by the proposed technique. The simulation and

experimental results also show that the new control method is not only easy to be calculated

and implemented, but also very effective in reducing harmonics.

REFERENCES

[1] Jarupula Somlal, Dr. Mannam Venu Gopala Rao “Analysis of Discrete & Space Vector

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[2] Jarupula Somlal, Dr. Mannam Venu Gopala Rao “Space Vector Modulated Hybrid

Active Power Filter for Power Conditioning”, i-manager’s Journal on Electrical

Engineering, October-December, 2011, ISSN: 2230–7176, Vol.5, No.2, pp.20- 26.

[3] An Luo, Zhikang Shuai, Wenji Zhu, Ruixiang Fan, and Chunming Tu, “Development of

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[7] N. Mohan, H. A. Peterson, W. F. Long, G. R. Dreifuerst, and J. J. Vithayathil, “Active

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[13] K. Nishida, Y. Konishi, and M. Nakaoka, “Current control implementation with

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AUTHORS

Somlal.J, at present is working as an Associate Professor in the department of

EEE, K.L.University, Guntur, Andhra Pradesh, India. He received B.Tech,

degree in Electrical and Electronics Engineering from J.N.T.University,

Hyderabad, A.P, India, M.Tech.,(Electrical Power Engineering) from

J.N.T.University, Hyderabad, A.P, India and currently working towards the

Doctoral degree in Electrical & Electronics Engineering at Acharya Nagarjuna University,

Guntur, Andhra Pradesh, India. He published 7 papers in National and International

Journals and presented various papers in National and International Conferences. His

research interests are in PWM, Fuzzy Logic and ANN applications to power system control

and power quality.

Dr.Venu Gopala Rao.M, FIE, MIEEE at present is Professor & Head,

department of Electrical & Electronics Engineering, K L University, Guntur,

Andhra Pradesh, India. He received B.E. degree in Electrical and Electronics

Engineering from Gulbarga University in 1996, M.E (Electrical Power

Engineering) from M S University, Baroda, India in 1999, M.Tech (Computer

Science) from JNT University, India in 2004 and Doctoral Degree in Electrical

& Electronics Engineering from J.N.T.University, Hyderabad, India in 2009. He published

more than 20 papers in various National, International Conferences and Journals. His

research interests accumulate in the area of Power Quality, Distribution System, High

Voltage Engineering and Electrical Machines.

Ramesh Matta, at present is working towards the M.Tech.,(Power Systems)

degree in the department of EEE, K.L.University, Guntur, Andhra Pradesh,

India. He received B.Tech, degree in Electrical and Electronics Engineering

from J.N.T.University, Kakinada, A.P, India.