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International Journal of Reviews in Computing

2009 IJRIC. All rights reserved. IJRIC

www.ijric.org E-ISSN: 2076-3336

25

NOVEL DEVELOPMENT OF A FUZZY CONTROL SCHEMEWITH UPFCs FOR DAMPING OF OSCILLATIONS IN

MULTI-MACHINE POWER SYSTEMS1 DAKKA OBULESU, 2Dr. S.F. KODAD, 3Dr. B.V. SANKAR RAM

1 Research Scholar, EEE Dept., JNTU, Hyderabad-85, Andhra Pradesh, India,Associate Professor, MITS, Madanapalli, Chittor Dist, AP, India.

Phone : +91 09441078630, Email : [email protected] , [email protected] 2 Professor & HOD, Dept. of EEE, Aurora Engg College, Bhongir-508116.

Phone: +91 09866666660, Email : [email protected] 3 Professor, Dept. of EEE, JNTUCE, Kukatpally, Hydarabad-85.

Phone: +91 09849303342, Email : [email protected]

ABSTRACT

This paper presents a novel development of a fuzzy logic controlled power system using UPFCs to dampthe oscillations in a FACTS based integrated multi-machine power system consisting of 3 generators, 3transformers, 9 buses, 4 loads & 2 UPFCs. Oscillations in power systems have to be taken a serious noteof when the fault takes place in any part of the system, else this might lead to the instability mode &shutting down of the power system. UPFC based POD controllers can be used to suppress the oscillationsupon the occurrence of a fault at the generator side or near the bus side. In order to improve the dynamic

performance of the multi-machine power system, the behavior of the UPFC based POD controller should becoordinated, otherwise the power system performance might be deteriorated. In order to keep theadvantages of the existing POD controller and to improve the UPFC-POD performance, a hybrid fuzzycoordination based controller can be used ahead of a UPFC based POD controller to increase the systemdynamical performance & to coordinate the UPFC-POD combination. This paper depicts about this hybridcombination of a fuzzy with a UPFC & POD control strategy to damp the electro-mechanical oscillations.The amplification part of the conventional controller is modified by the fuzzy coordination controller.Simulink models are developed with & without the hybrid controller. The 3 phase to ground symmetricalfault is made to occur near the first generator for 200 ms. Simulations are performed with & without thecontroller. The digital simulation results show the effectiveness of the method presented in this paper.

Keywords : UPFC , POD , Fuzzy logic, Coordination , Controller , Oscillations , Damping , Stability ,Simulink , State space model.

1. INTRODUCTION

Applications of ANN to power systems are agrowing area of interest. In the modern day power system stability, operation & control (PSOC),FACTS (Flexible AC Transmission Systems) playsa very important role. Usage of FACTS in the

power systems not only enhances the dynamic performance, but also increases the stability of the power systems, enhances the controllability &increases its power transfer capability. Some of thedevices used in the control of FACTS are the SVC,TCSC, STATCOM, UPFC, and the IPFC. TheFACTS controllers utilize power electronics basedtechnology and can provide dynamic control on line

power flows, bus voltages, line impedance & phase

angles. One of the controllers being used in thework presented in this paper is the UPFC basedfuzzy coordination scheme for the damping of

power system oscillations [1].

The FACTS initiative was originally launched

in 1980s to solve the emerging problems faced dueto restrictions on transmission line construction,and to facilitate growing power export / import andwheeling transactions among utilities. The two

basic objectives behind the development of FACTStechnology; is to increase power transfer capabilityof transmission systems, and to keep power flowover designated routes, significantly increase theutilization of existing (and new) transmissionassets, and play a major role in facilitating

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contractual power flow in electricity markets withminimal requirements for new transmission lines.

According to IEEE, FACTS - which is theabbreviation of Flexible AC Transmission Systems,is defined as alternating current transmissionsystems incorporating power electronics based andother static controllers to enhance controllabilityand power transfer capability. Dynamic reactive

power compensation and damping power systemoscillations can also be achieved using FACTScontrollers. Injecting the series voltage phasor,with desirable voltage magnitude and phase anglein a line can provide a powerful means of preciselycontrolling the active and reactive power flows, bywhich system stability can be improved, systemreliability can be enhanced while operating andtransmission investment cost can be reduced.

It is possible to vary the impedance of specifictransmission line to force power flow along adesired contract path in the emerging power systems, and to regulate the unwanted loop power flows and parallel power flows in theinterconnected system. The FACTS controllershave been broadly developed on two different

principles, one that alters the line series reactanceor bus shunt reactance or voltage phase differenceacross a line and utilizes conventional thyristor switches for control. In general, FACTS controllerscan be divided into four categories based on their connection in the network, viz., series, shunt,combined series-series, and combined series-shunt.In our work, we have used the series-shuntcombination [2].

FACTS devices have shown very promisingresults when used to improve the power systemsteady state performance. In addition, because of the extremely fast control action associated withFACTS-device operations, they have been very

promising candidates for utilization in power system damping enhancement. The first generationFACTS devices include SVC, TCPS, and TCSC. Ithas been found that SVCs can be effective indamping power system oscillations if asupplementary feedback signal is applied [11-12].Compared with other FACTS devices, littleattention has been paid to TCPS modeling andcontrol. Based on the equal area criterion, the TCPScontrol problem has also investigated using linear control techniques [13-15].

Many research efforts have been devoted to thecontrol of TCSC. Chen et. al. designed a statefeedback TCSC controller based on the pole

placement technique [16]. Other TCSC optimal andnonlinear control schemes proposed in the literature

[17-19]. A unified power flow controller (UPFC) isthe most promising device in the FACTS concept.Several trials have been reported in the literature tomodel a UPFC for steady-state and transientstudies. Based on NabaviIravani model [20],

Wang developed two UPFC models [21-23] whichhave been linearized and incorporated into theHeffron-Phillips model [3].

FACTS devices enhance the stability of the power system with its fast control characteristicsand continuous compensating capability. Thecontrolling of the power flow and increasing thetransmission capacity of the existing transmissionlines are the two main objectives of FACTStechnology [25]. Thus, the utilization of the existing

power system comes into optimal condition and thecontrollability of the power system is increasedwith these objectives. Gyugyi proposed the Unified

Power Flow Controller which is the new typegeneration of FACTS devices in the year 1991 [26].Unified Power Flow Controller (UPFC), being onethe member of the FACTS device thus emerged asone of the effective controllers for controlling andoptimization of the power flow in the electrical

power transmission systems [7]. This device wasformed due to the combination of the two other FACTS devices, namely Static SynchronousCompensator (STATCOM) and the StaticSynchronous Series Compensator (SSSC). Theseare connected to each other by a common DC link,which is a typical a storage capacitor. The all

parameters of the power transmission line(impedance, voltage and phase angle) can becontrol simultaneously by UPFC [28]. In addition,it can perform the control function of thetransmission line real / reactive power flow, UPFC

bus voltage and the shunt-reactive-power flowcontrol [29].

The control mechanism and the controller havean important effect on the performance of UPFC. Inthe literature, several control mechanisms are usedin UPFC models. A novel fuzzy inference systemdescribed in matrix form was proposed and used toimprove the dynamic control of real and reactive

power [30]. Two fuzzy logic controllers based onMamdani type fuzzy logic were used. In our work considered, we have used the Mamdani type fuzzylogic scheme for the control purposes [3].

The selection of suitable location for UPFC wasstudied and composite-criteria based fuzzy logicwas used to evaluate the network contingencyranking [31]. The power-feedback control schemeis used in the control mechanism of UPFC [32].The power fluctuation is damped readily and the

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value of reactive power is minimized as possible byusing several time constants. However, there is nochange in the value of the real power. The controlmethod of variable interval-fuzzy-mutual is used inthe control mechanism of UPFC [33]. The

performance of UPFCs is observed by using threedifferent controllers comparisons in [34-35].

In recent years, the fast progress in the field of power electronics has opened new opportunities for the power industry via utilization of the controllableFlexible AC Transmission System devices likeUnified Power Flow Controller which offer analternative means to mitigate power systemoscillations [5]. Oscillation Stability analysis andcontrol has been an important subject in power system research and applications. The deregulationand competitive environment in the contemporary

power networks [1, 2] will imply a new scenario in

terms of load and power flow condition and socausing problems of line transmission capacity.But, nowadays, some problems exist like power system oscillation stability & refers to the dampingof electromechanical oscillations occurring in

power systems with oscillation frequency in therange of 0.2 Hz. to 2 Hz. These low-frequencyoscillations are the consequence of the developmentof interconnection of large power systems. A lowfrequency oscillation in a power system constrainsthe capability of power transmission, threatenssystem security and damages the efficient operationof the power system [4].

Damping of electromechanical oscillations between interconnected machines in a integrated power system is always necessary for a securedsystem operation. A FACTS controller alwaysincreases the transmission capability and thestability. Researchers have developed a number of methods for damping of power system oscillationsusing FACTS devices. However, majority of themare confined to single machine infinite bus systems.Very few researchers have worked on multi-machine control of FACTS systems. Of course,this yields satisfactory results [6]. But, excellentresults can be obtained using the fuzzy logic

concepts, neural network concepts & the geneticalgorithms. This has been showed by fewresearchers in their papers [10], [36]. In this paper,we make a modest attempt to simulate a fuzzy logiccontrol scheme with a UPFC for a FACTS power system to dampen the power system oscillations[7].

The fuzzy logic control technique has been anactive research topic in automation and controltheory since the work of Mamdani proposed in

1974 based on the fuzzy sets theory of Zadeh proposed in 1965 to deal with the system control problems which are not easy to be modeled [36].The concept of FLC is to utilize the qualitativeknowledge of a system to design a practical

controller. For a process control system, a fuzzycontrol algorithm embeds the intuition andexperience of an operator designer and researcher.The control doesnt need accurate mathematicalmodel of a plant, and therefore, it suits well to a

process where the systems with uncertain or complex dynamics. Of course, fuzzy controlalgorithm can be developed by adaptation based onlearning and fuzzy model of the plant [14]. Thefuzzy control is basically non-linear and adaptive innature, giving robust performance under parameter variation and load disturbance effect [8].

In general, a fuzzy control algorithm consists of a

set of heuristic decision rules and can be regardedas an adaptive and non-mathematical controlalgorithm based on a linguistic process, in contrastto a conventional feedback control algorithm [3],[4]. Controlled Series Compensation for ImprovingStability of Multi-Machine Power Systems in [17].Fuzzy control using linguistic information

possesses several advantages such as robustness,model-free, universal approximation theorem andrules-based algorithm [5], [6]. Recent literature hasexplored the potentials of fuzzy control for machinedrive application [7], [8]. It has been shown that a

properly designed direct fuzzy controller can out-

perform conventional proportional integralderivative (PID) controllers [8]. Note that fuzzylogic controllers are nothing but rule-basedcontrollers in which a set of rules representing acontrol decision mechanism to adjust the effect of certain cases coming from power system isconsidered. Further, these FLCs do not require amathematical model of the system & can cover awide range of operating conditions with muchrobustness inherency. FLCs combined with UPFCcan definitely reduce the POD in multi-machinesystems [9].

In an interconnected power system, the

synchronous generators should rotate at the samespeed and power flows over tie-lines should remainconstant under normal operating conditions.However, low frequency electromechanicaloscillations may occur when a disturbance isapplied to the power system. These oscillations can

be observed in most power system variables like bus voltage, line current, generator rate and power.Power system oscillations were first observed assoon as synchronous generators were

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interconnected to provide more generation capacityand more reliability to a power system [10].

Originally, the fairly closely connectedgenerators were observed to swing against eachother at frequencies of around 1 2 Hz. Damper windings on the generators rotor were used to

prevent the amplitude of oscillations fromincreasing. After fast excitation systems wereintroduced to prevent the generators from loosingsynchronism following a system is fault, it wasnoticed that this kind of excitation system alwaystends to reduce the damping of the systemoscillations. Advanced tuning of POD controllersfor EPS using FACTS to damp the oscillations was

presented by Korba et.al . [37].

The organization of the paper is as follows.Firstly, a brief introduction to the FACTS, itsevolutions, applications, the UPFC controller & thefuzzy logic controller was presented in the previous

paragraphs. Section 2 presents the mathematicalmodeling of the multi-machine system along withthe parameters. In section 3, the control strategy,the design of the controller is presented. Section 4

presents the development of the Simulink model for the damping of the power system oscillations.Simulation results are presented in section 5.Conclusions are finally presented at the end insection 6. This is followed by the acronyms & thereferences.

2. MODELING OF THE 3-MACHINE, 9-BUSINTEGRATED POWER SYSTEM

The integrated multi-machine power systemmodel consisting of 3 generators used for thesimulation purposes is shown in the form of a one-line diagram (single line diagram) with & withoutthe controllers in the Figs. 1 & 2 respectively [10].The generators 1, 2 and 3 are connected to buses 1,5 and 8. Two UPFCs are used for controlling &damping the power system oscillations in theintegrated plant [10]. One is connected between

bus 2 & 3 and the other is connected between buses6 and 7.

Three transformers T1 to T3 are also used inthe integrated power system near the generator

buses for the power transmission purposes, i.e., for stepping up & stepping down purposes.Transmission lines are connected between the buses3-9-4-6. Since, we know that the power system is adynamic one, definitely, it is a non-linear system.For modeling & simulation purposes in the Matlab-Simulink environment, a numerical model isneeded. By linearizing about an operating point,

the total linearized power system model (the plantmodel) is represented finally in the state space formas [10]

u,D xC y

u,B xAx

+=+=&

(1)

where, x is the state vector of length equal to thenumber of states n, y is the output vector of lengthm, u is the input vector of length r , A is thesystem state matrix of size ( n n), B is the controlinput vector of size ( n r ), C is the control inputvector of size ( m n), D is the feed-forward matrixof size ( m r ).

Note that for this linearized power systemmodel, controllers are designed and put in loop withthe plant model so that when there are anydisturbances taking place like the faults, the

oscillations are reduced quickly in no time, thus becoming a closed loop feedback control system[12]. The simulation parameters of the 3-machine,9-bus interconnected power system model with 4loads L1 to L4 & 2 UPFCs is given in table I.

Gen 1

Bus 1 Transformer 1

B u s

2

Bus 3

Load 1 B u s

9

Bus 4 Transformer 3

Gen 3

Bus 5

Load 3

Gen 2

Bus 8 Transformer

2

Bus 7 Bus 6

Load 2

Load 4

Fig. 1 : A 3-machine, 9-bus interconnected power systemmodel with 4-loads without the controllers

Gen 1

Bus 1 Transformer 1

B u s 2

FuzzyLogic

Controller

Bus 3

Load 1 B u s

9

Bus 4 Transformer 3

Gen 3

Bus 5

Load 3

Gen 2

Bus 8 Transformer 2

Bus 7 Bus 6

Load 2

Load 4

UPFC1

POD 1

FuzzyLogic

Controller

UPFC2

POD 2

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Fig. 2 : A 3-machine, 9-bus interconnected power systemmodel with 4-loads & 2 POD-UPFC & the fuzzy

controller

Base value MVAS KV V B B 100,220 ==Generators

.).(25.0

.),.(17.0

.).(9.1

.),.(06.1

.).(2

.),.(56.1

6

,49.4

,0.0

102,822

3

21

3

21

3

21

03

0201

321

321

u p X

u p X X

u p X

u p X X

u p X

u p X X

sT

sT T

D D D

H s H H

d

d d

q

qq

d

d d

d

d d

===

===

===

===

======

Transformers .).(305.0321 u p j X X X T T T ===Transmissionlines

.).(25.00.0321 u p j Z Z Z l l l +===

UPFCs

Oper se

Oper seOper

V V

V V KV V

1.0

,1.0,220

min

max

===

Loads

.).(65.0

.),.(05.0

4

321

u p L

u p L L L=

===

Table I : Machine parameter details

3. DEVELOPMENT OF THECONTROL STRATEGY

A controller is a device which controls each &every operation in the system making decisions.From the control system point of view, it is

bringing stability to the system when there is adisturbance or a noise or a fault, thus safeguardingthe equipment from further damages. It may behardware based controller or a software basedcontroller or a combination of both. In this section,the development of the control strategy for dampingthe oscillations in FACTS based power systems is

presented.

The unified power flow controller (UPFC) is

one of the most promising device & most versatilecontroller device used in the FACTS family [1] -[4]. It is an electrical device for providing fast-acting reactive power compensation on high-voltage transmission networks & is generally usedas a controller in loop with the plant. It has thecapability to control voltage magnitude, lineimpedance and phase angle, and can alsoindependently provide (positive or negative)reactive power injections, thus providing thevoltage support, control of power flow, & can be

used to control active and reactive power flows in atransmission line. [5].

The concept of UPFC makes it possible tohandle practically all power flow control andtransmission line compensation problems, usingsolid state controllers, which provide functionalflexibility, generally not attainable by conventionalthyristor controlled systems [13]. It has a capabilityof improving both steady-state and dynamic

performances of a power system as they allow moreaccurate control of the power flow, better and faster control of voltage and system stability. [6].

It can be connected in series with atransmission line inside a system or in a tie-lineconnecting sub-systems in a large interconnected

power system [7]. Moreover, UPFC further improves the dynamic performance of the power system in coordination with damping controllers[8]. As a result, one of their applications is thedamping of power system oscillations using Power Oscillation Damping (POD), which recently has

been attracting the interest of many researchers,including ours [10].

The heart of the UPFC is the power electronicdevices, i.e., the silicon controlled rectifiers. Itconsists of two solid-state synchronous voltagesource converters coupled through a common DClink capacitor as shown in Fig. 3, i.e., 2 voltagesource inverters sharing a common DC storagecapacitor. Two coupling transformers are used. As

shown in Fig. 3, the UPFC consists of a boostingtransformer and an excitation transformer linked by back-to-back converters 1 and 2. The firstconverter 1 is connected in shunt and the secondone 2 in series with the line. The shunt converter is

primarily used to provide active power demand of the series converter through a common DC link [1],[2].

Converter 1 can also generate or absorbreactive power and thereby provide independentshunt reactive compensation for the line. Converter 2 provides the main function of the UPFC byinjecting additional voltage with controllable

magnitude and phase angle in series with thetransmission line through series transformer [26].The main task of the UPFC is to control the flow of

power in steady-state conditions. In addition, highspeed of operation of thyristor devices makes it

possible to control real and reactive power flow.The UPFC can be employed to enhance power system damping by modulating the converter voltages. The UPFCs are used at certain locationsin a integrated power system in between some

buses [3].

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The expanded model of the UPFC with itsinner details used for the dynamic simualtion in theSimulink is shown in the Fig. 4. In the Fig. 3shown, the i and j represents the buses in theintegrated power system model. In our work

considered, we have taken the total number of buses to be 9 & Two UPFCs connected between bus 2, 3 and 6, 7.

i j

SeriesTransformer

S h u n t

T r a n s

f o r m e r

Converter 1

Converter 2

Shuntside

Seriesside

DCLink

Capacitor

C

Fig. 3 : A block diagram of the UPFC scheme used inFACTS (single line diagram)

C

Series Transformer S h u n t T r a n s f o r m e r

U P F C C o n t r o l l e r I n p u t sI n

p u t s

C o n t r o l l e r 1 C o n t r o l l e r 2

A C A C

D C

T r a n s m i s s i o n l i n e

Fig. 4 : Expanded view of the UPFC scheme used inFACTS

UPFCs can be operated in a number of modes.Some of them are just mentioned here for the sakeof convenience. 2 control modes are used for theshunt control, viz., the VAR control mode and theautomatic voltage control mode. 4 control modesare used for the series control, viz., the directvoltage injection mode, phase angle shifter

emulation mode, the line impedance emulationmode & the automatic power flow control mode.Wang et.al. [9] has used the 3 control schemeconcept in his paper, viz., the uncontrolled mode,the inductive mode & the capacitive mode [17]. Inthe work considered in this paper for the dampingof the power system oscillations, UPFC is operatedin the direct voltage injection mode. Since theUPFC output is in the series compensation mode(V se), the voltage is perpendicular to the line current( I line) and the phase angle of the line current is

ahead of V se. By the control of the V se, the seriescompensating damping control can be achieved in acoordinated manner [18].

There are a lot of advantages of UPFC schemesused in FACTS. In the conventional methods -FACTS + POD controller + UPFC, the settling timemay be more, oscillations may be more, ringingeffects may be more. But, by the usage of POD &UPFC in coordiantion with a fuzzy controller in theFACTS based power systems, the settling time, theoscillations, the ringing effects of various power system parameters would be still less then the

previous counter-part (still improved with accuratedamping). But, still accurate results can beobtained when the POD based UPFC scheme iscoordinated with a fuzzy scheme. Hence, thisconcept, i.e., the control of power systemoscillations with fuzzy scheme & POD - UPFC is

used in our paper. This trio concept not onlyimproves the dynamics & the characteristics of the

power system, but also increases the stability. Thiscan be observed in the simulation results presentedin section 5 in this paper [19].

There are a lot of advantages of UPFC schemesused in FACTS. In the conventional methods -FACTS + POD controller + UPFC, the settling timemay be more, oscillations may be more, ringingeffects may be more. But, by the usage of POD &UPFC in coordiantion with a fuzzy controller in theFACTS based power systems, the settling time, theoscillations, the ringing effects of various power system parameters would be still less then the

previous counter-part (still improved with accuratedamping). But, still accurate results can beobtained when the POD based UPFC scheme iscoordinated with a fuzzy scheme. Hence, thisconcept, i.e., the control of power systemoscillations with fuzzy scheme & POD - UPFC isused in our paper. This trio concept not onlyimproves the dynamics & the characteristics of the

power system, but also increases the stability. Thiscan be observed in the simulation results presentedin section 5 in this paper [19].

In the POD-UPFC base controllers for theFACTS systems, performance is very good for single machine infinite bus problems; the dampingof the power system oscillations may be perfectlyachieved. But, for the multi-machine problems,using POD based UPFC controllers; the

performance characteristics may be deteriorated &it may swing into the instability mode, especiallywhen large faults & disturbances takes place. Inthis case, in order to achieve desired performanceof the multi-model system using the POD based

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UPFC controller, one has to take the coordination between the POD-UPFC controllers. This is verymuch essential, because a single POD-UPFCcontroller is used for the control of different

parameters in a single machine. For multi-machine

system, definitely, one has to use multiple POD-UPFC controllers for controlling the oscillations.Then, the coordination between the different POD

based UPFC controllers plays a very important rolein the stability of the power systems [20]. Toachieve excellent coordination between the various

parameters of the POD based UPFC controllers inFACTS, the fuzzy logics can be used along with thePOD-UPFC controller, which yields excellent

performance. This type of hybrid controller concept is considered in order to keep the existingPOD based UPFC controller performance andfurther improve its control performance using thefuzzy scheme [21].

In the research work considered in this paper,fuzzy logic controller is used to coordinate betweenthe various parameters of the multi-machine basedFACTS power system along with the POD-UPFCas shown in the block diagram in the Fig. 5. Thesefuzzy controllers have got a lot of advantagescompared to the classical (P, PI, PID) & theconventional controllers (POD-UPFC), such as thesimplicity of control, low cost, high reliability,compactness of the hardware (since fuzzy logiccontroller just makes use of fuzzy rules) and the

possibility to design without knowing the exact

mathematical model of the process [22].Fuzzy logic is one of the successful

applications of fuzzy set in which the variables arelinguistic rather than the numeric variables &emerged as a consequence of the 1965 proposal of fuzzy set theory by Lotfi Zadeh. Linguisticvariables, defined as variables whose values aresentences in a natural language (such as large or small), may be represented by the fuzzy sets.Fuzzy set is an extension of a crisp set where anelement can only belong to a set (full membership)or not belong at all (no membership). Fuzzy setsallow partial membership, which means that an

element may partially belong to more than one set.A fuzzy set A of a universe of discourse X isrepresented by a collection of ordered pairs of generic element x X and its membership function

: X [ 0 1], which associates a number A( x) :

X [ 0 1], to each element x of X . A fuzzy logiccontroller is based on a set of control rules called asthe fuzzy rules among the linguistic variables [27].These rules are expressed in the form of conditionalstatements. Our basic structure of the fuzzy logiccoordination controller to damp out the oscillations

in the power system consists of 3 important parts,viz., fuzzification, knowledge base - decisionmaking logic (inference system) and the de-fuzzification, which are explained in brief asfollows [23].

R u l e

b a s e

D e c

i s i o n

M a k

i n g

U n i

t

F u z z i

f i c a

t i o n

U n i

t

D e -

f u z z

i f i c a t

i o n

U n i

t

D a t a

b a s e

F u z z i

f i c a

t i o n

U n i

t

P O D

A c t u a l o u t p u t ( P o w e r a n g l e )

R e f e r

e n c e

U P F C

P o w e r

S y s

t e m

( P l a n t

)

F u z z y c o o r

d i n a

t i o n c o n t r o

l l e r

Fig. 5 : A diagrammatic view of a typical fuzzy logiccontroller used along with POD-UPFC for controlling the

oscillations in a power system

The internal structure of the fuzzy coordinationunit is shown in the Fig. 5. The necessary inputs tothe decision-making unit blocks are the rule-basedunits and the data based block units. Thefuzzification unit converts the crisp data intolinguistic variables. The decision making unitdecides in the linguistic variables with the help of logical linguistic rules supplied by the rule baseunit and the relevant data supplied by the data base[8[, [5]. The output of the decision-making unit is

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given as input to the de-fuzzification unit and thelinguistic variables of the signal are converted back into the numeric form of data in the crisp form [5].The decision-making unit uses the conditional rulesof IF-THEN-ELSE, which can be observed from

the algorithm mentioned below [24]. In thefuzzification process, i.e., in the first stage, the crispvariables P UPFC-1 and P UPFC-2 are converted intofuzzy variables or the linguistics variables [7].

The fuzzification maps the 2 input variables tolinguistic labels of the fuzzy sets. The fuzzycoordinated controller uses the linguistic labels :{(Small S mf 1), (Medium M mf 1), (Big B mf 2)}.Each fuzzy label has an associated membershipfunction. The membership function of triangular type is used in our work & is shown in the Fig. 6.The inputs are fuzzified using the 3-fuzzy sets (S,M, B). Power inputs of UPFC 1 and UPFC 2 are

given to fuzzy controller. The output of the fuzzy-converter will genarate the pulses, which are further given as inputs to the POD [25].

PUPFC-1

PUPFC-2

FUZZYSVPWM

(Mandani)

Ouput

Fig. 6 : FIS Fuzzy editor with 2 inputs and 1 outputs

The output of the fuzzy-converter will genaratethe pulses, which are further given as inputs to thePOD [25]. The membership function of the smallset is given by the Eqn. (2) as [10]

>+

+

++++

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