Article(id=1321491397186793777, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491360620851250, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2025.7.136, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1661184000000, receivedDateStr=2022-08-23, revisedDate=1676822400000, revisedDateStr=2023-02-20, acceptedDate=1677600000000, acceptedDateStr=2023-03-01, onlineDate=1792997701211, onlineDateStr=2026-10-26, pubDate=1764432000000, pubDateStr=2025-11-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792997701211, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792997701211, creator=13701087609, updateTime=1792997701211, updator=13701087609, issue=Issue{id=1321491360620851250, tenantId=1146029695717560320, journalId=1146031654075715584, year='2025', volume='23', issue='7', pageStart='1', pageEnd='348', issueExtLink='null', onlineDate='null', pubDate='1764432000000', pubDateStr='2025-11-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792997692494, creator='13701087609', updateTime=1792997936183, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321492382776931282, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491360620851250, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321492382776931283, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491360620851250, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=136, endPage=142, ext={EN=ArticleExt(id=1321491399141339442, articleId=1321491397186793777, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=MPPT Control of Photovoltaic Power Generation System Based on AC Reinforcement Learning, columnId=1152281492550987902, journalTitle=Journal of Power Supply, columnName=Renewable Energy System, runingTitle=null, highlight=null, articleAbstract=

Under special conditions such as rapid changes in solar irradiance, local shading or poor solar irradiance, the traditional method will cause a photovoltaic (PV) power generation system to oscillate near the maximum power point (MPP), and the tracking speed will be slow. In serious cases, the PV power generation system will exhibit severe power fluctuations, which will lead to divergence. To solve this problem, a maximum power point tracking (MPPT) control method for PV power generation systems based on reinforcement learning (RL) is proposed in this paper. The RBF neural network weight coefficient in RL is adjusted online and in real time to adapt to the output control signal, so that the active power output from PV power generation can be quickly tracked to the MPP. A PV power generation system was constructed in MATLAB/Simulink, and a comparison with the conventional RBF neural network control method was performed. Results show that the RL control can make the PV power generation system converge to the MPP stably and accurately in a shorter time. At the same time, the PV power generation system can output the maximum active power.

, authors=Laibao YU^{1, 2}, Xingwang XIE³, authorsList=Laibao YU, Xingwang XIE, authorCompany=null, correspAuthors=Laibao YU, 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=1321491399502049587, articleId=1321491397186793777, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于AC强化学习的光伏发电系统MPPT控制, columnId=1149829992055595012, journalTitle=电源学报, columnName=新能源系统, runingTitle=null, highlight=null, articleAbstract=

光伏发电系统中，当出现太阳辐照度变化快、局部遮阴或者辐照度较差等特殊情况时，采用传统方法会导致光伏发电系统在最大功率点附近振荡，跟踪速度变慢，严重时光伏发电系统会出现功率剧烈波动、难以收敛的现象。针对该问题，提出1种基于强化学习RL（reinforcement learning）的光伏发电系统最大功率点追踪MPPT（maximum power point tracking）控制方法，通过在线实时调节强化学习中径向基函数RBF（radial basis function）神经网络权值系数来自适应输出控制信号，让光伏发电输出的有功功率快速跟踪到最大功率点。在MATLAB/Simulink环境下搭建光伏发电系统，采用所提强化学习控制方法与常规RBF神经网络控制方法进行对比，结果验证了采用强化学习控制能够在更短时间内使得光伏发电系统快速、稳定、准确地收敛到最大功率点，以实现光伏发电输出功率的最大化。

, authors=于来宝^{1, 2}, 谢兴旺³, authorsList=于来宝, 谢兴旺, authorCompany=null, correspAuthors=于来宝, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=A3OHmmNHjCj8OLUdQNc5RA==, magXml=9UKXKD92TDjW3T6+BA5OXQ==, pdfUrl=null, pdf=sONq8TMnoln4BiAHZRb0AQ==, pdfFileSize=1460338, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=yqQZXFttEHwZ2bE6Fge+uw==, mapNumber=null, fund=null)}, authors=[Author(id=1321491400898752830, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=328426165@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=1, authorType=1, ext={EN=AuthorExt(id=1321491401175576897, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, authorId=1321491400898752830, language=EN, stringName=Laibao YU, firstName=Laibao, middleName=null, lastName=YU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=^1.College of Electromechanical Engineering, Wuhan City Polytechnic, Wuhan 430070, China
^2.School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321491401259462978, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, authorId=1321491400898752830, language=CN, stringName=于来宝, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=^1.武汉城市职业学院机电工程学院，武汉　430070
^2.中国地质大学地球物理与空间信息学院，武汉　430074, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1321491400361881908, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, xref=1., ext=[AuthorCompanyExt(id=1321491400370270517, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, companyId=1321491400361881908, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=^1.College of Electromechanical Engineering, Wuhan City Polytechnic, Wuhan 430070, China), AuthorCompanyExt(id=1321491400378659126, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, companyId=1321491400361881908, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=^1.武汉城市职业学院机电工程学院，武汉　430070)]), AuthorCompany(id=1321491400475128119, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, xref=2., ext=[AuthorCompanyExt(id=1321491400525459768, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, companyId=1321491400475128119, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=^2.School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China), AuthorCompanyExt(id=1321491400533848377, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, companyId=1321491400475128119, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=^2.中国地质大学地球物理与空间信息学院，武汉　430074)])]), Author(id=1321491401515315524, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, orderNo=1, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=sivan_xie@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321491401645338951, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, authorId=1321491401515315524, language=EN, stringName=Xingwang XIE, firstName=Xingwang, middleName=null, lastName=XIE, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=³, address=^3.School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321491401968300360, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1321491397186793777, authorId=1321491401515315524, language=CN, stringName=谢兴旺, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=³, address=^3.武昌理工学院人工智能学院，武汉　430223, bio={"content":"

谢兴旺（1973—　），男，博士，讲师。研究方向：人工智能。E-mail：sivan_xie@163.com。

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谢兴旺（1973—　），男，博士，讲师。研究方向：人工智能。E-mail：sivan_xie@163.com。

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