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时间:2019-02-14
《相关蜜蜂双种群进化型云自适应遗传算法的电力系统多目标无功优化 》由会员上传分享,免费在线阅读,更多相关内容在工程资料-天天文库。
1、第53卷第5期电测与仪表V0I.53No.52016年3月10日ElectricalMeasurement&InstrumentationMar.10,2016基于蜜蜂双种群进化型云自适应遗传算法的电力系统多目标无功优化f周海忠,周步祥,何春渝,周岐杰,彭章刚,王精卫(1.四川大学电气信息学院,成都610065;2.国网四川达州供电公司,四川达州635000)摘要:针对遗传算法在求解多目标无功优化方面存在的缺陷,文章提出了基于蜜蜂双种群进化型云自适应遗传算法(doublebeepopulationevolutionarycloudadaptive
2、geneticalgorithm,BEPE—CAGA)。该算法根据蜜蜂双种群进化思想,引入了雄峰通过竞争参与交叉及雄峰与决定双峰群优秀遗传基因的蜂后交叉的策略,并结合正态云模型云滴的随机性和稳定倾向性特点对其进行改进,改善了算法陷人早熟的问题,提高了算法的收敛速度。建立了以有功网损最小、电压偏差最小及电压稳定裕度最大为目标的无功优化数学模型,并以BEPE—CAGA算法求解该模型。最后通过对IEEE14和IEEE30节点系统进行算例仿真,仿真结果验证了文章所提算法的有效性,同时也证明了该算法在收敛速度和优化效果上具有比基本GA算法和CAGA算法更佳
3、的性能。关键词:蜜蜂双种群;云自适应;多目标;无功优化;遗传算法中图分类号:TM933文献标识码:B文章编号:1001.1390(2016)05-0103-06Multi-objectivereactivepoweroptimizationforpowersystembasedondoublebeepopulationevolutionarycloudadaptivegeneticalgorithmZhouHaizhong,ZhouBuxiang,HeChunyu,ZhouQijie,PengZhanggang,WangJingwei(.Scho
4、olofElectricalEngineeringandInformation,SichuanUniversity,Chengdu610065,China2.StateGridDazhouPowerSupplyCompany,Dazhou635000,Sichuan,China)Abstract:Inthispaper,adoublebeepopulationevolutionarycloudadaptivegeneticalgorithm(BEPE—CAGA)isproposedtocopewiththelimitationofgenetica
5、lgorithmsinsolvingthemulti—objectivereactivepoweroptimization.Basedondoublebeepopulationevolutionarythought,aftertheintroductionofcompetitiontoparticipateinacrossbydronesanddronesanddecideddoubletsexcellentcrossbeegeneticstrategies,andthen,incombinationofthenormalcloudmodelcl
6、ouddropletcharacteristicsofrandomnessandstabletendency,thisalgorithmsolvedtheproblemofpre—matureconvergenceofgeneticalgorithmandspeededuptheconvergencespeed.Thispaperestablishedareactivepoweroptimizationmathematicalmodelwithminimumactivepowerloss,voltagedeviationminimumandmax
7、imumvoltagestabilitymargingoals,andBEPE—CAGAalgorithmwasusedtosolvethemode1.Finally,theexamplessimula-tionresultsofIEEE14andIEEE30nodesystemverifytheeffectivenessoftheproposedalgorithm,aswellasprovedthatthealgorithmwasbetterthanthebasicGAalgorithmandCAGAalgorithmperformanceon
8、theconvergencespeedandoptimizationresults.Keywords:doublebeepopulati
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