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基于改进猫群算法的分布式电源优化配置 Title:DistributedPowerSystemOptimizationConfigurationbasedonImprovedCatSwarmAlgorithm Abstract: Withtheincreasingdemandforcleanandsustainableenergy,distributedpowersystemshavegainedsignificantattention.Optimalconfigurationofdistributedpowersystemsplaysacrucialroleinenhancingsystemperformance,reliability,andefficiency.ThispaperpresentsanovelapproachfordistributedpowersystemoptimizationconfigurationusinganimprovedCatSwarmAlgorithm(CSA).Theproposedmethodaddressesthechallengesofpowersystemoptimization,suchaslocaloptimaandinefficientexplorationofsearchspace.Theperformanceoftheproposedmethodiscomparedwithotheroptimizationalgorithms,andtheresultsdemonstrateitseffectivenessinachievingoptimalpowersystemconfigurations.Thisstudycontributestothefieldofdistributedpowersystemsbyprovidingapracticalandefficientoptimizationapproachforpowersystemplanningandoperation. Keywords:Distributedpowersystem,optimizationconfiguration,CatSwarmAlgorithm,cleanenergy,systemperformance,reliability,efficiency,searchspace. 1.Introduction Distributedpowersystems(DPS)consistofmultiplesmall-scalepowergeneratorsconnectedtothegrid.Thesesystemshaveemergedasaviablesolutiontomeettheincreasingenergydemandwhilereducinggreenhousegasemissions.However,theoptimalconfigurationofdistributedpowersystemsplaysacrucialroleinenhancingtheirperformanceandefficiency.Effectiveoptimizationtechniquesarerequiredtodeterminetheoptimaldistributionofpowergeneratorsandtheirparametersinthesystem. 2.RelatedWork Previousresearchhasemployedvariousoptimizationalgorithms,suchasGeneticAlgorithms(GA),ParticleSwarmOptimization(PSO),andAntColonyOptimization(ACO),forpowersystemoptimizationconfiguration.Theseapproacheshaveshownpromisingresults,buttheystillsufferfromlimitationssuchasslowconvergence,prematureconvergence,andbeingtrappedinlocaloptima. 3.ProposedMethod Inthispaper,animprovedCatSwarmAlgorithm(CSA)isproposedfordistributedpowersystemoptimizationconfiguration.CSAisapopulation-basedmetaheuristicalgorithminspiredbythepr