Article(id=1321485664458228351, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321485607872872740, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2025.5.96, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1657987200000, receivedDateStr=2022-07-17, revisedDate=1663862400000, revisedDateStr=2022-09-23, acceptedDate=1670515200000, acceptedDateStr=2022-12-09, onlineDate=1792996334422, onlineDateStr=2026-10-26, pubDate=1759161600000, pubDateStr=2025-09-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792996334422, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792996334422, creator=13701087609, updateTime=1792996334422, updator=13701087609, issue=Issue{id=1321485607872872740, tenantId=1146029695717560320, journalId=1146031654075715584, year='2025', volume='23', issue='5', pageStart='1', pageEnd='332', issueExtLink='null', onlineDate='null', pubDate='1759161600000', pubDateStr='2025-09-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792996320932, creator='13701087609', updateTime=1792996886850, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321487981563728115, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321485607872872740, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321487981563728116, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321485607872872740, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=96, endPage=104, ext={EN=ArticleExt(id=1321485666572157568, articleId=1321485664458228351, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Research on MPPT Control Strategy Based on Improved PSO Algorithm Using Chaotic Mapping and Gaussian Perturbation, columnId=1152281492153004911, journalTitle=Journal of Power Supply, columnName=Modeling and Control, runingTitle=null, highlight=null, articleAbstract=

Under the partial shade condition of photovoltaic (PV) arrays, multiple peaks will appear on the P-U curve, and the traditional particle swarm optimization (PSO) algorithm cannot search for the maximum power point quickly and accurately. In view of this issue, a maximum power point tracking (MPPT) control strategy based on improved PSO algorithm using chaotic mapping and Gaussian perturbation is put forward. First, a chaotic Sine mapping was introduced to construct a nonlinear random incremental inertia weight, and Gaussian perturbation was introduced in the “individual cognition” part of the particle swarm. Meanwhile, a logarithmic function was used to construct a learning factor, thus forming an improved PSO algorithm based on chaotic mapping and Gaussian perturbation. Through the test of six typical unimodal and multimodal functions, it was verified that this algorithm converged faster and was less susceptible to local maxima. Then, the algorithm was applied to MPPT control, and comparative simulations of different algorithms on MPPT control were performed. The comparative simulation results show that in three cases of uniform light intensity, static partial shading and dynamic shading, the MPPT control strategy based on improved PSO algorithm using chaotic mapping and Gaussian perturbation has a faster convergence speed and a smaller search oscillation amplitude, and it can find the maximum power point with a higher searching precision, thereby improving the power generation efficiency of the MPPT system.

, authors=Yiping XIAO^{1, 2}, Yunfeng ZHAO¹, authorsList=Yiping XIAO, Yunfeng ZHAO, authorCompany=null, correspAuthors=Yunfeng ZHAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321485666970616451, articleId=1321485664458228351, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于混沌映射和高斯扰动的改进粒子群优化算法MPPT控制策略研究, columnId=1149829942550200325, journalTitle=电源学报, columnName=建模与控制, runingTitle=null, highlight=null, articleAbstract=

光伏阵列在局部阴影条件下P-U曲线会出现多个峰值，传统的粒子群优化PSO（particle swarm optimization）算法无法快速精确地搜寻到最大功率点。针对这种情况，本文提出1种基于混沌映射和高斯扰动的改进粒子群优化算法最大功率点跟踪MPPT（maximum power point tracking）控制策略。首先引入混沌Sine映射构造1种非线性随机递增惯性权重，并在粒子群的“个体认知”部分引入高斯扰动，同时利用对数函数构造学习因子，形成基于混沌映射和高斯扰动的改进粒子群算法；通过对6种典型单峰、多峰函数的测试，证明该算法收敛速度更快，不易陷入局部最优；将算法应用于MPPT控制中，并进一步通过不同算法MPPT控制进行对比仿真研究。对比仿真结果表明：在均匀光照强度、局部静态遮荫和动态遮荫3种情况下，基于混沌映射和高斯扰动的改进粒子群优化算法MPPT控制策略均具有更快的收敛速度和更小的搜索振荡幅度，能准确地搜寻到最大功率点，具有更高的寻优精度，从而提高了MPPT系统的发电效率。

, authors=肖义平中国电源学会会员^{1, 2}, 赵云峰¹, authorsList=肖义平中国电源学会会员, 赵云峰, authorCompany=null, correspAuthors=赵云峰, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=psDT1ldr36aKv6/Bfzrs9A==, magXml=3bsLpXjjtxNLsqvpdA+G8Q==, pdfUrl=null, pdf=4wQqNVhnrh2ubsgRj7CPhQ==, pdfFileSize=2919739, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=jn5knPn0vNO5lvVbTlJktg==, mapNumber=null, fund=null)}, authors=[Author(id=1321485667381658251, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321485664458228351, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=yp_xiao2013@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321485667507487374, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321485664458228351, authorId=1321485667381658251, language=EN, stringName=Yiping XIAO, firstName=Yiping, middleName=null, lastName=XIAO, prefix=null, suffix=Member, CPSS, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=^1.School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
^2.Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321485667784311439, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321485664458228351, authorId=1321485667381658251, language=CN, stringName=肖义平中国电源学会会员, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=^1.湖北工业大学电气与电子工程学院，武汉　430068
^2.湖北工业大学新能源及电网装备安全监测湖北省工程研究中心，武汉　430068, bio={"content":"

肖义平（1973—　），女，中国电源学会会员，博士，讲师。研究方向：新能源发电，微电网控制。E-mail：yp_xiao2013@163.com。

"}, bioImg=null, bioContent=

肖义平（1973—　），女，中国电源学会会员，博士，讲师。研究方向：新能源发电，微电网控制。E-mail：yp_xiao2013@163.com。

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