LOAD FREQUENCY CONTROL OF HYDRO - IJSET · LOAD FREQUENCY CONTROL OF HYDRO ‐ THERMAL AND NUCLEAR...
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LOAD FREQUENCY CONTROL OF HYDRO ‐ THERMAL AND NUCLEAR INTERCONNECTED POWER SYSTEM USING FUZZY AND ANN CONTROLLERS. 1 K. SUDHEER KUMAR, 2 A. PRASAD 1 P.G Scholar (EPS), Dept. of EEE, NEC ‐ Nellore, Andhra Pradesh, India, Email: [email protected] 2 Assistant Professor, Dept. of EEE, NEC ‐ Nellore, Andhra Pradesh, India, Email: [email protected] ABSTRACT This paper present analysis on dynamic performance of Load Frequency Control (LFC) of four area interconnected hydro‐ thermal and nuclear interconnected power system by the use of Artificial Intelligent, Fuzzy and PI Controller. In the proposed scheme, control methodology developed using conventional PI controller, Artificial Neural Network (ANN) and Fuzzy Logic controller (FLC) for four area interconnected hydro‐thermal and nuclear power system. In this paper area‐1 and area‐2 consists of thermal reheat power plant and area‐3 consists of hydro power plant whereas area‐4 consists of nuclear power plant. In this proposed scheme, the combination of most complicated system like hydro plant and thermal plant with reheat turbine and nuclear plant are interconnected which increases the nonlinearity of the system. The generator inertia and load values have been different for thermal–reheat plants, same inertia and load value for hydro and nuclear plant. The performance of the controllers is simulated using MATLAB/SIMULINK package. A comparison of PI controller, Fuzzy controller and ANN controller based approaches shows the superiority of proposed ANN based approach over Fuzzy and PI for same conditions. To improve the performance of PI, Fuzzy and neural controller sliding surface is incorporated. The simulation results also tabulated as a comparative performance in view of settling time and peak over shoot. Index Terms: Load Frequency Control (LFC), Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN) Controller, Proportional Integral (PI) controller, Area Control Error (ACE), Tie‐line power, MATLAB / SIMULINK. 1. INTRODUCTION Now a day’s population has been increased rapidly. Such that more population requires, more power with reliability and security. Power systems are very large and complex electrical networks consisting of generation networks, transmission networks and distribution networks along with loads which are being distributed throughout the network over a large geographical area. For that purpose, Electrical Power systems are interconnected to provide secure and economical operation. Power system is typically divided into control areas, with each consisting of one or more power utility companies. Sufficient supply for generation of each connected area to meet the load demand of its customers. Automatic Generation Control (AGC) or Load Frequency Control (LFC) is a very important issue in power systems for supplying reliable electric power with good quality [1, 2]. Automatic Load Frequency Control helps to diminish the transient deviations in addition to making the steady state error to zero. For successful operation of interconnected power system total generation should be equal to the total load demand plus system losses. A sudden load change in any area of interconnected power system causes the deviation of frequencies of all the areas. The main objectives of AGC are to maintain the megawatt output and the nominal frequency in an interconnected power system [3, 4]. Different types of control techniques such as classical control, variable structure control and robust control have been applied to the LFC problem [5]. Conventional PI controller is simpler for implementation but its settling time is more and it produces large frequency deviation. As an alternative to conventional PI controller, Fuzzy Logic Controller has been widely used for nonlinear and complex systems. However, it is demonstrated good dynamics only when selecting the specific number of membership function, so that the method had limitation. To overcome this Artificial Neural Network (ANN) controller, which is an advance adaptive control design, is used because the controller provides quicker control than the others. In this paper, the performance evaluation based on PI controller, Fuzzy controller and Artificial Neural controller for four area interconnected hydro ‐ thermal and nuclear power plant is proposed. The sliding concept arises due to variable structure concept. This paper is organized in five sections; the first section is the introduction part which is explained above. In section 2 how two areas are interconnected by tie line is shown and describes the mathematical modeling of the interconnected thermal‐hydro, nuclear power systems. Section 3 presents the design procedure of controllers used, in which conventional PI and fuzzy logic controller, Ann controller are discussed in detailed. Section 4 is devoted to the simulation models and simulation results. Conclusion is given in section 5. 2. FOUR AREA POWER SYSTEM INVESTIGATED The detailed designed model of four area hydro thermal and nuclear interconnected power system for load K. SUDHEER KUMAR et al. Citation: 10.2348/ijset06150582 ISSN (O): 2348-4098 ISSN (P): 2395-4752 International Journal of Science, Engineering and Technology- www.ijset.in 582
Transcript of LOAD FREQUENCY CONTROL OF HYDRO - IJSET · LOAD FREQUENCY CONTROL OF HYDRO ‐ THERMAL AND NUCLEAR...
LOADFREQUENCYCONTROLOFHYDRO‐THERMALANDNUCLEARINTERCONNECTEDPOWERSYSTEMUSINGFUZZYANDANNCONTROLLERS.
1K.SUDHEERKUMAR,2A.PRASAD
1P.GScholar(EPS),Dept.ofEEE,NEC‐Nellore,AndhraPradesh,India,Email:[email protected]
2AssistantProfessor,Dept.ofEEE,NEC‐Nellore,AndhraPradesh,India,Email:[email protected]
ABSTRACT
ThispaperpresentanalysisondynamicperformanceofLoadFrequencyControl (LFC)of fourarea interconnectedhydro‐thermalandnuclearinterconnectedpowersystembytheuseofArtificialIntelligent,FuzzyandPIController.Intheproposedscheme,controlmethodologydevelopedusingconventionalPIcontroller,ArtificialNeuralNetwork(ANN)andFuzzyLogiccontroller (FLC) for four area interconnected hydro‐thermal and nuclear power system. In this paper area‐1 and area‐2consistsofthermalreheatpowerplantandarea‐3consistsofhydropowerplantwhereasarea‐4consistsofnuclearpowerplant.Inthisproposedscheme,thecombinationofmostcomplicatedsystemlikehydroplantandthermalplantwithreheatturbineandnuclearplantareinterconnectedwhichincreasesthenonlinearityofthesystem.Thegeneratorinertiaandloadvalues have been different for thermal–reheat plants, same inertia and load value for hydro and nuclear plant. Theperformance of the controllers is simulated using MATLAB/SIMULINK package. A comparison of PI controller, FuzzycontrollerandANNcontrollerbasedapproachesshowsthesuperiorityofproposedANNbasedapproachoverFuzzyandPIforsame conditions. To improve the performance of PI, Fuzzy and neural controller sliding surface is incorporated. Thesimulationresultsalsotabulatedasacomparativeperformanceinviewofsettlingtimeandpeakovershoot.
IndexTerms: Load Frequency Control (LFC), Fuzzy Logic Controller (FLC),ArtificialNeuralNetwork (ANN) Controller,ProportionalIntegral(PI)controller,AreaControlError(ACE),Tie‐linepower,MATLAB/SIMULINK.
1.INTRODUCTION
Nowaday’spopulationhasbeen increasedrapidly.Suchthat more population requires, more power withreliabilityandsecurity.Powersystemsareverylargeandcomplex electrical networks consisting of generationnetworks, transmission networks and distributionnetworks along with loads which are being distributedthroughout the network over a large geographical area.For that purpose, Electrical Power systems areinterconnected to provide secure and economicaloperation.Powersystem is typicallydivided intocontrolareas,with each consistingof one ormorepowerutilitycompanies. Sufficient supply for generation of eachconnectedareatomeettheloaddemandofitscustomers.Automatic Generation Control (AGC) or Load FrequencyControl(LFC)isaveryimportantissueinpowersystemsforsupplyingreliableelectricpowerwithgoodquality[1,2].AutomaticLoadFrequencyControl helps todiminishthetransientdeviations inadditiontomakingthesteadystate error to zero. For successful operation ofinterconnectedpower system total generation shouldbeequal to the total load demand plus system losses. Asudden loadchange inanyareaof interconnectedpowersystemcausesthedeviationoffrequenciesofalltheareas.ThemainobjectivesofAGCaretomaintainthemegawattoutput and the nominal frequency in an interconnectedpowersystem[3,4].Differenttypesofcontroltechniquessuch as classical control, variable structure control androbustcontrolhavebeenappliedtotheLFCproblem[5].ConventionalPI controller is simpler for implementation
but its settling time is more and it produces largefrequencydeviation.Asanalternative toconventionalPIcontroller, Fuzzy Logic Controller has been widely usedfor nonlinear and complex systems. However, it isdemonstrated good dynamics only when selecting thespecific number of membership function, so that themethodhadlimitation.ToovercomethisArtificialNeuralNetwork (ANN)controller,which isanadvanceadaptivecontrol design, is used because the controller providesquicker control than the others. In this paper, theperformance evaluation based on PI controller, Fuzzycontroller and Artificial Neural controller for four areainterconnectedhydro ‐ thermalandnuclearpowerplantis proposed. The sliding concept arises due to variablestructureconcept.
Thispaperisorganizedinfivesections;thefirstsectionistheintroductionpartwhichisexplainedabove.Insection2how twoareasare interconnectedby tie line is shownand describes the mathematical modeling of theinterconnected thermal‐hydro, nuclear power systems.Section 3 presents the design procedure of controllersused, inwhichconventionalPIandfuzzylogiccontroller,Ann controller are discussed in detailed. Section 4 isdevotedtothesimulationmodelsandsimulationresults.Conclusionisgiveninsection5.
2.FOURAREAPOWERSYSTEMINVESTIGATED
Thedetaileddesignedmodel of four areahydro thermaland nuclear interconnected power system for load
K. SUDHEER KUMAR et al. Citation: 10.2348/ijset06150582ISSN (O): 2348-4098 ISSN (P): 2395-4752
International Journal of Science, Engineering and Technology- www.ijset.in 582
frequencycontrolinvestigatedisshowninFigure2.1.Thesystem parameter values are given in appendix. Belowsimulation block diagram represented for differentcontrollers such as PI Controller, Fuzzy Logic ControllerandANNcontrollerforfourareasystems.
Figure2.1:MATLABSimulinkmodelofhydro–thermalandnuclearinterconnectedpowersystem
Inthispaper,theperformanceevaluationbasedonANN,Fuzzy and conventional PI controller for four areasinterconnectedhydro–thermalandnuclearpowerplantis proposed. The sliding concept arises due to variablestructure concept. The purpose of VSC has been reallyextendedfromstabilizationtonewcontrolfunctions.Themostwell‐knownaspectofVSCisitscapabilitytoresultinvery robust control systems; in many cases invariantcontrolsystemresult.Theterm‘invariant’meansthatthesystem is completely insensitive to parametricuncertainty and external disturbances [12]. The aim ofcontrolareasareas:
(i)Eachcontrol areaas for aspossible shouldsupply itsown load demand and power transfer through tie lineshouldbeonmutualagreement.
(ii) Each control areas should controllable to thefrequencycontrol.[11]
In an isolated control area case the incremental power(ΔPG−ΔPD)wasaccountedforbytherateofincreaseofstoredkineticenergyandincreaseinarealoadcausedbyincrease in frequency. The MATLAB model of four areahydro‐thermalandnuclearinterconnectedpowersystemshowninfigure2.1.
Transferfunction(T/F)ofhydraulicTurbineis .
. .
T/FofhydraulicGovernoris . .
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.
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T/FofnuclearGovernoris .
T/FofnuclearHighpressureturbineis
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T/Fofnuclearlowpressureturbine1is .
ThestatvariableforeachofareasareΔPi(i=1,2,3,4)andstatespaceequationrelatedtothevariablesaredifferentforeachareas.
ΔP1(k)=ΔP12(k)+a41ΔP41(k)(6)
ΔP2(k)=ΔP23(k)+a12ΔP12(k)(7)
ΔP3(k)=ΔP34(k)+a23ΔP23(k)(8)
ΔP4(k)=ΔP41(k)+a34ΔP34(k)(9)
Tie‐linebiascontrolisusedtoreducesteadystateerrorinfrequencyintie‐linepowerflow.Thisstatesthattheeachcontrol area must contribute their share to frequencycontrol in addition for taking care of their own netinterchange.
Let ACE1=AreaControlErrorofarea1
ACE2=AreaControlErrorofarea2
ACE3=AreaControlErrorofarea3
ACE4=AreaControlErrorofarea4
In this control, ACE1, ACE2 and ACE3 are made linearcombinationoffrequencyandtielinepowererror[11].
ACE1=ΔP12+b1Δf1(10)
ACE2=ΔP23+b2Δf2(11)
ACE3=ΔP34+b3Δf3(12)
ACE4=ΔP41+b4Δf4(13)
Where the constant b1, b2, b3 and b4 are called areafrequency bias of area 1, area 2, area 3 and area 4respectively. Now ΔPR1, ΔPR2, ΔPR3 and ΔPR4 are modeintegral of ACE1, ACE2, ACE3 and ACE4 respectively.Controlmethodologyused (FLC&ANN) ismentioned inthenextPrecedingsections.
3.CONTROLMETHODOLOGY
3.1PICONTROLLER
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K. SUDHEER KUMAR et al. Citation: 10.2348/ijset06150582ISSN (O): 2348-4098 ISSN (P): 2395-4752
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The proposed network has been trained by using thelearning performance. Learning algorithms causes theadjustment of theweights so that the controlled systemgivesthedesiredresponse.
4.SIMULATIONANDRESULTS
In this presentedwork, four areashydro‐thermal reheatand nuclear interconnected power system have beendevelopedbyusingfuzzylogic,ANNandPIcontrollerstoillustratetheperformanceofloadfrequencycontrolusingMATLAB/SIMULINK package. The parameters used forsimulationaregiven inappendix.Four typesof simulinkmodelsaredevelopedusingfuzzy,ANN,andPIcontrollersto obtain better dynamic behavior. Frequency deviationplots for thermal and hydro and nuclear cases areobtained for 1% step load change in system frequencyand tie‐line power as shown in fig. 4.1 to 4.22respectively.
Figure 4.1 : Change in frequency for thermal plant with PIcontroller
Figure 4.2 : Change in frequency for Hydro plant with PIcontroller
Figure 4.3 : Change in frequency for Nuclear plant with PIcontroller
Figure 4.4 : Change in Tie‐line power for Thermal‐thermalplantwithPIcontroller
Figure4.5 :ChangeinTie‐linepowerforThermal‐hydroplantwithPIcontroller
Figure4.6 :ChangeinTie‐linepowerforhydro‐nuclear plantwithPIcontroller
Figure4.7:ChangeinTie‐linepowerfornuclear‐nuclearplantwithPIcontroller
Figure 4.8 : Change in frequencies for thermal‐ thermalhydro‐nuclearPlantwithPIcontroller.
Figure4.9 : Change in frequency for thermalplantwithFuzzycontroller
Figure4.10 : Change in frequency for hydro plantwith Fuzzycontroller
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Change
inFrequency(Hz)
ChangeinTie‐linepow
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ChangeinTie‐linepow
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Figure4.11 :ChangeinfrequencyfornuclearplantwithFuzzycontroller
Figure 4.12 : Change in Tie‐line power for Thermal‐thermalplantwithFuzzycontroller
Figure 4.13 : Change in Tie‐line power for thermal ‐ hydroplantwithFuzzycontroller
Figure4.14:ChangeinTie‐linepowerforhydro‐nuclearplantwithFuzzycontroller
Figure 4.15: Change in frequencies for thermal‐ thermalhydro‐nuclearPlantwithFuzzycontroller.
Figure4.16:ChangeinfrequencyforthermalplantwithNarmacontroller
Figure4.17 :Change in frequency forhydroplantwithNarmacontroller
Figure4.18:ChangeinfrequencyfornuclearplantwithNarmacontroller
Figure 4.19 : Change in Tie‐ line power for thermal‐ thermalplantwithNarmacontroller
Figure4.20:ChangeinTie‐linepowerforthermal‐hydroplantwithNarmacontroller
Figure4.21:ChangeinTie‐linepowerforhydro‐nuclearplantwithNarmacontroller
Figure 4.22 : Change in frequencies for thermal‐ thermalhydro‐nuclearPlantwithNarmacontroller.
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International Journal of Science, Engineering and Technology- www.ijset.in 587
With 1% load variation in power system the followingresults are obtained. Conventional PI and Intelligent(Fuzzy and ANN ) control approach with inclusion ofslider gain provides better dynamic performance andreduces the steady state error and oscillation of thefrequency deviation and the tie line power flow in eacharea in hydro‐thermal and nuclear combination of fourarea interconnected power system. Settling time andmaximumpeakovershootintransientconditionforbothchangeinsystemfrequencyandchangeintie‐linepoweraregivenintable4.1,4.2,4.3&4.4respectively.
Table4.1Comparativestudyofsettlingtime
Table4.2Comparativestudyofsettlingtime
Table4.3Comparativestudyofsettlingtime
Table4.4Comparativestudyofsettlingtime
5.CONCLUSIONS
Form theabove table4.1,4.2,4.3and4.4 it is clear thatANNcontrollerwith sliding gainprovidesbetter settlingperformancethanFuzzyandconventionalone.Therefore,the intelligent control approach using ANN concept ismore accurate and faster than the PI and fuzzy control
scheme even for complex dynamical system. In future,ANFIScontrollerisutilizedinthissystem.
APPENDIX
f = 50 Hz ; R1 =R2= R3= R4 =2.4 Hz/ per unit MW,Tgi=0.08sec,Tpi=20sec;Ptie,max=200MW;Tr=10sec; Kr = 0.5, Ki1 = Ki2= 0.35, H1 =H2 =H3= H4 =5 sec ;Pri=2000MW ;Tti =0.3sec ;Kp1=200Hz.p.u/MW ;kp2=50Hz.p.u/MW, Kp3 = Kp4 = 120 Hz.p.u/MW;Tp1=40sec;Tp2=10sec;Tp3=Tp4=20sec;Ki=5.0;Tw=1.0sec;Di=8.33*10‐3p.uMW/Hz.;B1=B2=B3=B4=0.425p.u. MW/hz; ai=0.545;a=2*pi*T12=2*pi*T23=2*pi*T34=2*pi*T41= 0.545, delPd1=dePd2=delPd3=delPd4=0.01
NOMENCLATURE
F=Frequency, i =Subscript related to area (i=1,2,3,4),f =Nominal system frequency, H =Inertia constant,ΔP =Incremental load change, ΔP Incrementalgeneration change, T = Steam governor time constant,K =Reheat constant, T =Reheat time constant,T = Steam turbine time constant, R = Governor speedregulation parameter, B = Frequency bias constant,T =2Hi/f*Di,K :1/Di,K =FeedbackgainofFLC,Δδ=Changeinangle,ΔP=Changeinpower,Δf=Changeinsupply frequency,R=Speed regulationof thegovernor,K =Gainofspeedgovernor,T =Timeconstantofspeedgovernor ,K = 1/B= Power system gain,T = 2H/Bf0 =Powersystemtimeconstant.
REFERENCES
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BIOGRAPHIES
K.SUDHEER KUMAR is a P.GScholar in Electrical Powersystems, Narayana EngineeringCollege, Nellore, AndhraPradesh, India. His area ofinterests in Power systems ,PowerElectronics.
A.PRASAD is working asAssistant Professor in Dept. ofEEE, Narayana EngineeringCollege, Nellore, AndhraPradesh, India. His area ofinterests in Power systems,PowerElectronics.
K. SUDHEER KUMAR et al. Citation: 10.2348/ijset06150582ISSN (O): 2348-4098 ISSN (P): 2395-4752
International Journal of Science, Engineering and Technology- www.ijset.in 589
