Article(id=1309639771559912097, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639744087229134, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1707062400000, receivedDateStr=2024-02-05, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172053470, onlineDateStr=2026-09-23, pubDate=1768838400000, pubDateStr=2026-01-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172053470, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172053470, creator=13701087609, updateTime=1790172053470, updator=13701087609, issue=Issue{id=1309639744087229134, tenantId=1146029695717560320, journalId=1146119893612605453, year='2026', volume='44', issue='1', pageStart='1', pageEnd='142', issueExtLink='null', onlineDate='null', pubDate='1768838400000', pubDateStr='2026-01-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790172046920, creator='13701087609', updateTime=1790231697005, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309889934790590725, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639744087229134, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309889934790590726, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1309639744087229134, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=114, endPage=121, ext={EN=ArticleExt(id=1309639771719295650, articleId=1309639771559912097, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=Improved coati algorithm bifurcation disturbance suppression strategy for active disturbance rejection control, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problem of output voltage oscillation and deterioration of power quality caused by parameter changes of Boost cascade converter, an active disturbance rejection suppression strategy that improved coati algorithm is proposed. First, a simplified discrete model of the cascade converter was established to prove the existence of period-doubling bifurcation in system and combined with simulation verification, it was concluded that the low -frequency oscillation of the port output voltage was due to the period-doubling bifurcation disturbance in the system; Secondly, using linear active disturbance rejection controller compensates for disturbance estimation. Since manual tuning of parameters is inefficient and difficult to ensure accuracy, improved coati algorithm is introduced to realize adaptive tuning of controller parameters. In terms of algorithm, SPM chaos mapping is used for initialization to increase population diversity; Incorporating Levy flight and refractive backward learning strategies to avoid the algorithm falling into local optimum and to improve the efficiency and quality of parameter finding. Finally, through simulation verification and analysis, improved coati algorithm parameter configuration is more accurate and efficient, and the tuned controller can effectively suppress period -doubling bifurcation disturbances and improve the quality of transmitted power.

, authors=Borui Sun, Xuesong Zhou, Youjie Ma, authorsList=Borui Sun, Xuesong Zhou, Youjie Ma, authorCompany=null, correspAuthors=Xuesong Zhou, 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=1309639771908039331, articleId=1309639771559912097, tenantId=1146029695717560320, journalId=1146119893612605453, language=CN, title=改进浣熊算法的自抗扰控制分岔扰动抑制策略, columnId=0, journalTitle=可再生能源, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对Boost级联变换器受参数变化导致输出电压振荡恶化电能质量的问题，文章提出一种改进浣熊算法的自抗扰抑制策略。首先，建立级联变换器的简化离散模型，证明系统中存在倍周期分岔并结合仿真验证，得出引起端口输出电压产生低频振荡是由于系统发生了倍周期分岔扰动；其次，利用线性自抗扰控制器对扰动估计补偿，由于人工整定参数低效且难以保证精确性，引入改进浣熊算法实现控制器参数自适应整定，算法方面，采用自参数化（SPM）混沌映射初始化，增加种群多样性，融合Levy飞行和折射反向学习策略避免算法陷入局部最优，提高参数寻优效率和质量；最后，仿真验证了改进浣熊算法参数配置整定后的控制器能够有效抑制倍周期分岔扰动，改善传输电能质量。

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