Article(id=1309639749275583230, 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=1725206400000, receivedDateStr=2024-09-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172048156, onlineDateStr=2026-09-23, pubDate=1768838400000, pubDateStr=2026-01-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172048156, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172048156, creator=13701087609, updateTime=1790172048156, 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=70, endPage=77, ext={EN=ArticleExt(id=1309639749569184511, articleId=1309639749275583230, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=Cross-wind state evaluation of wind turbine based on multi-sensor data fusion, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to explore the strain characteristics of wind turbine blades during continuous crosswind, a multi -sensor data fusion method for wind turbine crosswind state evaluation is proposed in this study. In this method, ensemble Empirical Mode Decomposition (EMD) and composite multi-scale permutation entropy-wavelet algorithm are used to jointly reduce the noise of wind turbine blade strain signals, and then Kernel Principal Component Analysis (KPCA) is used to integrate the multiple groups of strain signals after noise reduction. The Squared prediction error (SPE) statistic was used as the evaluation index to effectively divide the wind turbine crosswind process. The results show that the proposed method is effective in reducing the noise of non-stationary wind turbine blade strain signal, and can accurately reflect the strain variation law in continuous crosswind process. In addition, through the combination of KPCA and SPE statistics, the process of wind turbine crosswind is segmtioned, and the operating state of wind turbine crosswind under different influencing factors is analyzed.

, authors=Yongbo Li¹, Zhen Liu^{1, 2, 3}, Jianwen Wang^{2, 4}, Mengnan Zheng¹, Hongyu Liu¹, authorsList=Yongbo Li, Zhen Liu, Jianwen Wang, Mengnan Zheng, Hongyu Liu, authorCompany=null, correspAuthors=Zhen Liu, 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=1309639749757928192, articleId=1309639749275583230, tenantId=1146029695717560320, journalId=1146119893612605453, language=CN, title=多传感器数据融合的风力机侧风状态评估, columnId=0, journalTitle=可再生能源, columnName=, runingTitle=null, highlight=null, articleAbstract=

为了探索连续侧风过程中风力机叶片的应变特性，文章提出了一种多传感器数据融合的风力机侧风状态评估方法。该方法首先采用集合经验模态分解-复合多尺度排列熵-小波算法对风力机叶片应变信号进行联合降噪，再用核主成分分析（KPCA）对降噪后的多组应变信号进行融合，将平方预测误差（SPE）统计量作为评估指标，有效划分风力机侧风状态。结果表明，所提方法对于非平稳风力机叶片应变信号降噪效果明显，能够准确反映连续侧风状态下的应变变化规律。此外，文章将KPCA和SPE统计量结合，对风力机侧风运行状态进行分类，对不同影响因素下的风力机侧风运行状态进行了分析。

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