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基于CAMOCS算法的多目标无功优化.docx

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基于CAMOCS算法的多目标无功优化 Title:Multi-objectiveReactivePowerOptimizationbasedonCAMOCSAlgorithm Abstract: Inmodernpowersystems,reactivepoweroptimizationplaysacrucialroleinensuringtheefficientandreliableoperationofthegrid.Thisoptimizationprobleminvolvesmultipleconflictingobjectives,includingvoltagestability,powerlossminimization,andvoltagedeviationreduction.Thispaperproposesanovelapproachtotacklethemulti-objectivereactivepoweroptimizationproblemusingtheCAMOCS(ComprehensiveLearningParticleSwarmOptimizationwithCrowdingDistanceSorting)algorithm.TheCAMOCSalgorithmcombinestheparticleswarmoptimization(PSO)andcrowdingdistancesortingtechniquestoprovideacomprehensivesolution. 1.Introduction: Reactivepoweroptimizationisanessentialaspectofpowersystemcontrol,aimedatachievingmaximumpowertransfercapability,reducingpowerlosses,andimprovingvoltagestability.Traditionally,reactivepoweroptimizationwasconsideredasasingle-objectiveproblem,consideringonlyoneoptimizationcriterion.However,inmodernpowersystems,multipleobjectivesneedtobesimultaneouslyoptimizedtoensureoptimaloperation.Thispaperfocusesonthemulti-objectivereactivepoweroptimizationproblem. 2.ProblemFormulation: Themulti-objectivereactivepoweroptimizationproblemcanbeformulatedasfollows: MinimizeF1(Q),F2(Q),...,Fn(Q) Subjectto: -Powerflowequations -Voltagestabilityconstraints -Operatinglimitsofcontrolvariables WhereF1(Q),F2(Q),...,Fn(Q)representsthenobjectivefunctions,Qdenotesthevectorofcontrolvariables(suchasreactivepoweroutputsofgeneratorsandcapacitorbanks),andtheconstraintsensurethefeasibleoperationofthepowersystem. 3.CAMOCSAlgorithm: TheCAMOCSalgorithmisahybridmetaheuristicalgorithmthatcombinesthestrengthsofparticleswarmoptimization(PSO)andcrowdingdistancesortingtechniques.Itenhancesthediversityandconvergenceofthesearchprocess,makingitwell-suitedformulti-objectiveoptimizationproblems.Thealgorithmconsistsofthefollowingsteps: a.Initialization:Initializethepopulationofparticleswithrandompositionsandvelocitieswithinthefeasiblerange. b.Fitn