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时间:2020-03-28
《基于自适应混沌粒子群优化算法的多目标无功优化.pdf》由会员上传分享,免费在线阅读,更多相关内容在行业资料-天天文库。
1、第39卷第9期电力系统保护与控制Vb1.39No.92011年5月1日PowerSystemProtectionandControlMay1,20l1基于自适应混沌粒子群优化算法的多目标无功优化李娟,杨琳,刘金龙,杨德龙,张晨(1.东北电力大学电气工程学院,吉林吉林132012;2.华北电力大学电气与电子工程学院,北京102206)摘要:针对粒子群无功优化中由于随机生成代表控制变量值的粒子,使得在优化迭代过程中易陷入局部最优解,而且后期收敛速度慢等问题,将混沌优化算法融合到粒子群算法中,提出了混沌粒子群算法求解多目标无功优化问题。该算法在初始
2、化粒子即无功优化控制变量值时,采用混沌思想,增加控制变量取值的多样性;通过粒子群无功优化算法计算各个粒子对应的适应值即无功优化目标函数值,并按照其大小择优选取控制变量值进行混沌优化以帮助无功优化控制变量跳出局部极值区域;并根据无功优化目标函数值自适应地调整其惯性权重系数以提高全局与局部搜索能力。通过算例分析表明,采用自适应混沌粒子群算法进行无功优化,能够及时跳出局部最优得到全局最优解,且收敛速度快。关键词:自适应;混沌粒子群优化算法;无功优化;惯性权重Multi-objeetivereactivepoweroptimizationbasedo
3、nadaptivechaosparticleswarmoptimizationalgorithmLIJuan1,YANGLin,LIUJin.1ong,YANGDe.1ong2ZHANGChen2,(1.SchoolofElectricalEngineering,NortheastDianliUniversity,Jilin132012,China;2.SchoolofElectricalandElectronicsEngineering,NorthChinaElectricPowerUniversity,BeUing102206,China
4、)Abstract:Particleswarmalgorithmusedinreactivepoweroptimizationalwaysfallsintolocaloptimalsolutionandfinalslowconvergenceduetogeneratingparticlesascontrollingvariablevaluesrandomly.Consequently,byintegratingthechaoticopitimizationalgorithmintotheparticleswarmalgorithm,anewa
5、daptivechaoticparticleswarmoptimizationbasedonchaostheoryisadoptedtosolvetheproblem.Throughtheusingofchaosergodicityfirstly,thecontrolvariablesinthesystemareinitializedtoenhancethediversityofparticlepopul~ions.Foreachiterationupdateofthegroup,theindividualparticle’Sfimessva
6、lue,namelythereactivepoweroptimizationobjectivefunctionvalueiscalculated,andaccordingtotheirsizessomeparticlesareselectedtobetreatedwithchaosoptimizationtohelpthereactivepoweroptimizationcontrollingvariablestojampoutofthelocalextremeregions;accordingtoeachparticle’Sfitnessv
7、alue,itsinertiaweighcoeficientisadjustedadaptivelytoenhancetheentiregroupofglobalandlocalsearchcapabilities.Throughcalculationandanalysisofcases,theresultsshowthatadaptivechaoticparticleswarmalgorithmusedinreactivepoweroptimizationcalljumpoutoflocaloptimumintimetofindtheglo
8、baloptimalsolutionandcompletefastconvergence·Keywords:adaptive;chaoticparticleswar
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