预览加载中，请您耐心等待几秒...

基于改进多重极限学习机的槽电压优化方法.docx

预览
1/3
2/3
3/3

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

基于改进多重极限学习机的槽电压优化方法 Title:ANovelSlotVoltageOptimizationMethodbasedonImprovedMultipleDeepLearningMachines Abstract: Theslotvoltageoptimizationisacrucialaspectofpowersystemoperationandcontrolasitdirectlyinfluencesthestabilityandefficiencyofpowertransmissionanddistribution.Inthispaper,weproposeanovelslotvoltageoptimizationmethodbasedonanimprovedmultipledeeplearningmachines(MDLMs).Theproposedmethodaimstoenhancetheaccuracyandefficiencyofpowersystemslotvoltageoptimization,tacklingthechallengesposedbycomplexpowersystemdynamicsanduncertainties.Themethodencompassesthreekeysteps:datapreprocessing,improvedMDLMtraining,andslotvoltageoptimization.Extensivesimulationexperimentswereconductedonarealisticpowersystemnetwork,demonstratingthesuperiorityoftheproposedmethodintermsofconvergencespeedandaccuracycomparedtoexistingmethods. 1.Introduction Theoptimizationofslotvoltageplaysapivotalroleinpowersystemoperationandcontrol.Anappropriateslotvoltagenotonlyensuresthestabilityofpowertransmissionanddistributionbutalsoimprovessystemefficiency.Traditionaloptimizationmethodsoftenrelyonmathematicalmodels,whichmaybelimitedbytheaccuracyofthesemodelswhenfacedwithcomplexsystemdynamicsanduncertainties.Toovercometheselimitations,theapplicationofdeeplearningtechniqueshasshowngreatpotentialinsolvingpowersystemoptimizationproblems.ThispaperpresentsanimprovedMDLM-basedslotvoltageoptimizationmethod,whichcaneffectivelyhandlethecomplexitiesanduncertaintiesofpowersystems. 2.ProblemFormulation Theslotvoltageoptimizationproblemisformulatedtofindtheoptimalvoltagesettingsforeachslotinthepowersystemnetwork.Theobjectiveistominimizetheoverallpowerlosswhilesatisfyingvariousconstraints,suchasvoltagelimitsandreactivepowerlimits.Traditionally,thisproblemissolvedusingmathematicaloptimizationalgorithms,whichmaynotbecapableofhandlingcomplexanduncertainpowersystemdynamics.Deeplearningtechniqueshaveemergedasapromisingalternativeforsolvingsuchoptimizationproblemsduetotheircapacitytolearncomplexpatternsfromlargeamountsofdata