design of an adaptive neuro fuzzy logic controller …design of an adaptive neuro fuzzy logic...

Design of an adaptive neuro fuzzy logiccontroller for the unified power flow

controller used in real and reactive powercompensation and power oscillation

damping in a single machine infinite busbar system

K.A.Rani Fathima1 and T.A.Raghavendiran2

1Professor, Department of Engineering/Electrical SectionAl-Musanna College of Technology

Al-Muladdah - [email protected]

2Principal, Anand Institute of higher technology,Chennai, Tamilnadu, India

[email protected]

December 23, 2017

Abstract

It is possible that as constrained by the operational re-quirements the single machine infinite bus bar system is fre-quently subjected to sudden real and reactive power load-ing and also as a result of sudden inclusion or exclusionof sources and loads connected to the grid, it may exhibitlow frequency Power Oscillations. Though the traditionalProportional integral (PI) controller can handle this prob-lem the associated non linearities and the lack of precisemathematical models of sub systems it warrants that someintelligent control techniques are necessary so that the sys-tem offers seamless stable operation. The Adaptive Neuro

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International Journal of Pure and Applied MathematicsVolume 118 No. 16 2018, 851-871ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

851

Fuzzy inference system (ANFIS) is a good candidate since itcan handle problems associated with systems for which theexact mathematical model is either not available or what isavailable is approximate or too complex. In this article it isshown with MATLAB / SIMULINK based simulation thatthe Adaptive Neuro Fuzzy Inference System can offer a bet-ter operational performance compared to the conventionalPI controller.

Keywords The Unified Power Flow Controller(UPFC), Adaptive Neuro Fuzzy Inference System(ANFIS), Single Machine Infinite Bus (SMIB) barpower system.

1 INTRODUCTION

Power system stability is an important requirement which is usuallyunder threat because of a number of reasons usually associatedwith transients. To start with, from the instant a single machinegenerating system is connected to a grid system and as the demandin the bus bar changes from time to time the power system stabilityis affected. The frequent changes in the power system parameterslike impedance of the grid, the voltage profile and the phase ofthe power system voltage and the interaction of all these threeparameters with the single machine contribute much for the adverseeffects on the operational conditions of the system at large [1].

If stability is required in the face of the changes in the importantparametric variations of the grid then suitable control systems areabsolutely essential and if such systems are intelligent and adaptivethen the system can guarantee operational stability and therebyreliability [2-4].

The UPFC basically has two Graetz bridge converters each witha DC and a three phase AC sides sharing a common DC link. One ofthese two converters is the shunt converter and it is responsible forreactive power support relieving the single machine from reactiveloading. The other converter is a series converter that governs thereal and reactive power flow into the grid. The shunt converter ispopularly known as the Static Synchronous Compensator (STAT-COM) and the series converter is known as the Static Synchronous

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International Journal of Pure and Applied Mathematics Special Issue

852

Series Compensator (SSSC). Each of these converters are individu-ally controlled by a set of two independent or decoupled controllers,and it is at this control system the PI or PID controllers are in usein the existing systems. In [5, 6], the author demonstrates thelinearization and application of Phillips-Heffron model in a powersystem for analyzing the damping effect in Flexible alternating cur-rent transmission system (FACTS) devices.

This paper describes the step by step procedure for the devel-opment of the proposed Adaptive Neuro Fuzzy Inference System tobe used in the shunt and the series converters.

As for the existing control systems available in literature a feware considered in the chronological order to get an insight into theexisting systems. FACTS systems came into existence since 1980when Hingorani published his first paper on FACTS systems. Fol-lowed by the inception of FACTS systems many scholars have sig-nificantly contributed in this field [7-11]. In [12, 13] the authorshave demonstrated how a PI controller can be used in the man-agement of an UPFC. If properly designed with the exact math-ematical model of the system under control the PI controller willgive satisfactory results. However the PI controllers require propertuning which is matter of experience than mathematical formula-tions. Most PI controller tuning techniques including the Zeiglerand Nichols method are empirical. Intelligent optimization tech-niques have also been used in the tuning of the PI controllers andin [1, 14, 15] the authors have demonstrated how the Genetic Algo-rithm (GA) can be used for tuning the PI controller. The ParticleSwarm Optimization (PSO) is used in the tuning of a PI controlleras found in [16, 17].

Thus besides using the PI controller with empirical tuning tech-niques like the Zeigler Nichols procedure numerous techniques basedon soft computing systems are also found in the literature. Di-rect intelligent control systems without the PI control systems havealso been employed in systems demonstrated by various authors asfound in [18-21].

In [22-25] the authors have used an artificial Neural Networkbased controller for the management of the UPFC. The main ad-vantage of using the intelligent controllers is that the system can bedesigned easily without the mathematical model of the system butwith the experience on the system. While the Fuzzy logic controller

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(FLC) uses third party experience presented in the form of a knowl-edge base or the so called rule base the Artificial Neural Networkgains its own experience by way of learning. The FLC is good as aclosed loop controller and the ANN is capable of thoroughly learn-ing the system behavior in all its details of characteristics, evenbetter than a human operator when these two are combined a farbetter control system can be realized and this paper is all aboutthe combination of an FLC with the advantages of the ANN andthat is called the ANFIS.

Next to this brief introduction section 2 gives an outline of theUPFC and the controllers associated with them. Section 3 gives thefundamentals of the ANFIS. The MATLAB / SIMULINK basedsimulation in various sub systems and the results discussed are pre-sented in section 4 followed by conclusion.

2 UNIFIED POWER FLOW CONTROLLER

The UPFC is a power electronic system that helps the power systemcome out of the effects of disturbances while ensuring the possiblepower system requirements as set by the operational demands thatmay vary from time to time. The UPFC by itself is not an auxiliarysource of electrical power catering to sudden demands though it cando it for short durations. Under transient conditions following sys-tem disturbances caused by inclusion or exclusion of new loads andsources in respect of the grid, the UPFC can help the power systemto quickly move towards a new equilibrium state as compared tothe previously maintained equilibrium state.

Basically if a system in equilibrium is disturbed then it mayoscillate for some time and eventually as the oscillations die outthe system may settle at an equilibrium state. After the oscillationsdie out, if the values of the state variables are new as compared tothe values of the state variables before disturbance then the systemhas assumed a new equilibrium state In some applications some ofthe parameters should be maintained at the same value in boththe old and the new equilibrium states (eg. Voltage) while some(eg. Current) can be changed. Those parameters that can undergosome change and assume new values at the new equilibrium stateare called free parameters, subject to some practical limits. In the

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single machine infinite bus bar system, which is a closed systemthe required real power is expected to be pumped into the grid in asmooth fashion without fluctuations or power ripples or oscillations.The UPFC is an extension or an attachment to the SMIB systemthat facilitates the smooth flow of power from the single machineto the grid. With the UPFC attached to the SMIB system thenthe system as a whole becomes a more flexible system with moredegrees of freedom to help maintain the basic system parametersclose to the set values by manipulating the controllers associatedwith the converters of the UPFC. The structure and location of theUPFC in SMIB is shown in Fig. 1.

A.Modeling of an UPFCThe basic objective of any power transmission system is to de-

liver the required real and reactive power to the receiving end.While this is done there should not be oscillations happening aboutthe steady state requirements. The UPFC is required only toachieve the objectives of the power delivery systems. Further withrespect to classical electrical theory there are three things that areusually manipulated for achieving proper power delivery. The ma-nipulating factors are the load side voltage, the phase angle of theload side voltage and the impedance of the load and feeder com-bined as viewed from the point of common coupling.

These three parameters can be manipulated by the UPFC. As aresult the UPFC can guarantee the required power transfer, voltageprofile and the source power factor. The reactive power demand ofthe load is met by the shunt component of the UPFC while theseries converters ensure the delivery of real power demand of theload.The UPFC is the composition of two back to back converters with acommon DC link. The DC link is to be maintained at the requiredvoltage level and this voltage has to be maintained at a constantlevel throughout the operation of the UPFC. When the series com-pensator attempts to deliver real power the DC link voltage willtend to fall down and the same should be topped up appropriatelyby the shunt converter.

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Figure 1: Structure and location of the UPFC in a Single MachineInfinite Bus Bar System

B. The Shunt converterThe shunt converter is a three phase Graetz bridge arrangement

with three legs and two switches in each bridge. IGBTs of appro-priate voltage and current ratings are ideal though there are otherswitching devices available in the market.

As for the shunt converter, commonly known as the STATCOM,there are two degrees of freedom. The modulation index and thephase angle of the modulating signal are the two parameters thatare adjusted to drive the STATCOM to deliver the required reactivepower to the point of common coupling and to maintain the DClink voltage at the required level.

The shunt converter obeys Kirchhoffs current law. Figs. 2 and3 show the transaction of real and reactive power between the pointof common coupling and the terminals of the STATCOM converter.If V1 and V2 are the voltages prevailing at nodes A and B then thereactive power flow between the nodes A and B is given in

Q = (V1(V1−V2))X

cos δ (1)

Where X is the reactance between nodes A and B and is theangle between the voltages V1 and V2. According to this relation-ship if = 0 then the magnitude and direction of reactive powertransaction will be decided only by the difference in voltages.

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Figure 2: Reactive power flow by difference of voltage

Similarly if V1 and V2 are the voltages prevailing at nodes Aand B then the real power flow between the nodes A and B is givenin (2),

P = V1V2X

sin δ (2)

Where X is the reactance between nodes A and B and is theangle between the voltages V1 and V2. According to this relation-ship even if V1=V2 then the magnitude and direction of real powertransaction will be decided only by the difference in the phase anglemaintained between nodal voltages V1 and V2.

C. The Series converterThe series converter is meant to inject the required voltage in

series with the feeder. The series converter element of the UPFCcan inject a series voltage component with appropriate magnitudeand phase angle. The series converter also has two degrees of free-dom viz. the modulation index and phase angle of the referencesignal to be used in the Pulse Width Modulation (PWM) for theseries converter. In the design of the proposed control scheme threeof the basic objectives of the UPFC are considered. These three arethe guarantee of the requireda. Real power delivery to the receiving end

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b. the guarantee of the required real power delivery to the receivingend andc. damping of low frequency oscillations usually observed at thesending and receiving ends pertaining to power transactions withsudden changes in power flow commands.

Figure 3: Real power flow with difference in phase angle

The series element of the UPFC is also known as the boost ele-ment while the shunt element of the UPFC is known as the exciterelement.

D. The controllers associated with the STATCOMAs for the shunt converter there are two controllers. These two

controllers are meant for controlling two controlled parameters andthese controllers produce two manipulated parameters. The con-trolled parameters of the shunt converter control system are the dcomponent of the Park transformed voltage at the point of commoncoupling and the DC link voltage. The respective manipulated pa-rameters are MI and Theta of the reference signal to be used inthe PWM operation. The general control structure for the shuntconverter is as shown in Fig. 4.

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Figure 4: A general control structure

The series converter can insert a series voltage for all the threephases at a certain angle decided by the controllers. This is depictedin Fig. 5. The resultant voltage with an altered amplitude andangle will then appear at the receiving end terminals.

Figure 5: Block diagram of the series converter and vector diagramof the series converter

E. System dynamics and the phillips heffron model with UPFCPower system damping studies can best be carried out with the

help of state space modeling. The Single Machine Infinite Bus bar

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system is a third order system and it can be represented by a lin-earised model as shown below. Fig.6. shows the complete systemwith a single machine and an infinite bus bar with the UPFC lo-cated near the load side. The two converters of the UPFC knownas the shunt converter and the series converter are called the excit-ing converter and the boost converter. Therefore in the rest of thispaper suffixes e and b are used to denote the various parametersassociated with the excitation and boost converters.

Figure 6: UPFC with SMIB

Figure 7: Phillips Heffron Model with UPFC

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The three phase quantities, for example, Vabc correspondingto excitation and boost systems are park transformed to vde, vqe,vdb and vqb. These quantities can be related by the followingequations.[

νEtdνEtq

]=

[0 −xExE 0

] [iEdiEq

]+

[mE cos δEνdc

2mE sin δEνdc

2

](3)

[νBtdνBtq

]=

[0 −xBxB 0

] [iBdiBq

]+

[mB cos δBνdc

2mB sin δBνdc

2

](4)

νdc = 3mE4Cdc

[cos δE sin δE

] [iEdiEq

]+

3mB4Cdc

[cos δB sin δB

] [iBdiBq

](5)

The park transformed excitation voltage, excitation current,boosting voltage, and boosting current are related in the state spacematrices. Cdc and Vdc are the DC link capacitance and voltage re-spectively.

The non linear equations of the power system under considera-tion (SMIB) are

δ = ω0(ω − 1) (6)

ω = (Pm − Pe −D∆ω)/M (7)

E′q = (−Eq+Efd)/Td0 (8)

Efd = (−Efd+KA(Vref−Vt))/Ta (9)

ν = jxtEit+νEt (10)

νEt = νBt+jxBviB+νb (11)

where it and νb are the armature current and infinite bus volt-age, respectively and νEt , νBt, iB and iE are the ET voltage, BTvoltage, BT current and ET current respectively

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∆δ∆ω

∆E′q

∆Efd∆νdc

=

0 ω0 0 0 0

− k1M

− DM

− k2M

0 −kpdM

− k4Td0

0 − k3Td0

1Td0

− kqdTd0

−kAk5TA

0 −kAk6TA

1TA

−kAkvdTA

k7 0 k8 0 −k9

∆δ∆ω∆E

′q

∆Efd∆νdc

+

0 0 0 0

−kpeM

−kpδeM

−kpbM

−kpδbM

− kqe

T′d0

−kpδe

T′d0

− kqb

T′d0

kpδb

T′d0

−kAkνeTA

−kAkνδeTA

−kAkνbTA

−kAkνδbTA

kce kcδe kcb kcδb

∆mE

∆δE∆mB

∆δB

(13)

With reference to (13) the dynamic quantities or state variablesunder consideration or observation are given in a column vectoralong the State Matrix. The system constants and the influence ofeach of them on the other are given by the State Matrix. The inputsare the suggested changes in the modulation indices and delta ofthe shunt and the series converters.

The State Space model suggests that the state variables willtend to attain a state of new equilibrium with the variations ofthe values of the state variables tending to become zero and thatwill happen as dictated by the input parameters viz. modulationindices and delta of the shunt and the series converters.

3 ADAPTIVE NEURO FUZZY INFER-

ENCE SYSTEM

In situations of non linearities and approximations the Fuzzy LogicControl (FLC) scheme is promising. The FLC being a control tech-nique to deal with approximate data to arrive at an agreeable resultit can be put under service where there is an expectation with a cer-tain range or degree of acceptability. In the management of UPFCas found in ref [20, 25] the FLC can offer a stable operation trackingthe set points and keeping the actual values within close vicinity ofthe desired set points.

The fuzzy logic system in its basic form has the following steps.They are Fuzzyfication, Inference and decision making using therule base and defuzzification. Out of these three basic steps of

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the FLC the Fuzzification process and the Inference and decisionmaking process depend largely on the perception and experience ofthe designer. Thus the performance of the FLC is a function of theexperience of the operator and hence the entire control system is atthe disposal of the designer. If something can be done about thisthe performance of the FLC can be drastically improved.

In ANFIS, a neural network is framed to do the job of learningand gaining experience. Once the learning process is over the Neu-ral Network can be set to decide on the ranges for various linguisticvariables and their overlap. In ANFIS system the ANN is used tocoin the rules based on the experimental data supplied to the ANN.Thus if the capability of an ANN is incorporated with the FLC thedrawbacks of the conventional FLC of being reliant on the designeris alleviated.

A. Training data collection In the present design four FLCs werefirst designed and the system was put under service. There werea set of four error-error rate outputs. The MATLAB model ofthe UPFC for the SMIB system was run and the four sets of datawere collected with some disturbances to the system with changesin load etc. The individual data sets of these four data sets wereindividually used to train an ANFIS unit and thus four units of AN-FIS were developed. The related screen shots as they appear stepafter in MATLAB SIMULINK environment are shown in Figs. 8-10

Figure 8: The ANFIS training page of MATLAB/ SIMULINK AN-FIS tool.

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Figure 9: ANFIS structure.

Figure 10: ANFIS in action.

4 RESULTS AND DISCUSSIONS

The results of simulation using both FLC and ANFIS are comparedin Table 1. The power transaction waveforms are given in Fig. 12.

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Figure 11: Simulink model of UPFC.

Figure 12: The comparison of the source side and load side pa-rameters like KVA, KW,KVAR, PF and the DC link voltage forANFIS

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Figure 13: THD spectrum for voltage at point of common couplingfor ANFIS.

Table 1: Performance comparison of two controllers for UPFCParameter Fuzzy ANFISP delivered to Load 1.4 1.457Q Delivered to Load 1.02 1.034PF at Source 0.94 0.99THD at PCC 1.69% 1.18%

Figure 14: THD spectrum for voltage at point of common couplingfor FLC.

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5 CONCLUSION

A novel ANFIS based control system was proposed for the manage-ment of UPFC. The proposed system was tested in the MATLAB/ SIMULINK environment and the results validated the proposal.The dynamic performance in terms of power transaction and damp-ing of oscillations were also found to be better than the conventionalFLC system. The system can be extended for multiple machine en-vironments as well.

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design of an adaptive neuro fuzzy logic controller …design of an adaptive neuro fuzzy logic...

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