Article(id=1223210587988017532, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221389, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1657209600000, receivedDateStr=2022-07-08, revisedDate=1660579200000, revisedDateStr=2022-08-16, acceptedDate=null, acceptedDateStr=null, onlineDate=1769565730330, onlineDateStr=2026-01-28, pubDate=1687622400000, pubDateStr=2023-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769565730330, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769565730330, creator=13701087609, updateTime=1769565730330, updator=13701087609, issue=Issue{id=1223210584024400210, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='6', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769565729385, creator=13701087609, updateTime=1769593153259, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223325608164348105, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223325608164348106, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=175, endPage=179, ext={EN=ArticleExt(id=1223210588654911881, articleId=1223210587988017532, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Prediction of Vibration Trend of Hydroelectric Unit Based on WOA-VMD-TCN, columnId=1222925284869922957, journalTitle=Water Resources and Power, columnName=ELECTROMECHANICS AND CONTROL ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

Prediction of the vibration trend of hydropower units is an important measure to ensure the normal operation of the unit. However, due to the complexity and non-stationarity of the vibration signal of the unit, accurate prediction becomes a difficult problem. To this end, this paper proposes a combined trend prediction model based on adaptive variational modal decomposition and temporal convolutional network (TCN). Firstly, the Whale Swarm Algorithm (WOA) was used to optimize the parameters of Variational Mode Decomposition (VMD) to avoid the drawbacks of blindly selecting parameters, and to achieve adaptive decomposition of vibration signals. And then each decomposed component signal was normalized to establish TCN for trend prediction. Finally the original vibration signal prediction was obtained by superimposing the results. The proposed model was demonstrated and tested with the actual operation data of a domestic power station. The results show that the proposed model has high prediction accuracy and can be used in engineering practice.

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水电机组振动趋势预测是保障机组正常运行的重要举措，但由于机组振动信号的复杂性和非平稳性使得准确预测成为难题。为此，提出一种基于自适应变分模态分解和时间卷积网络（TCN）的组合趋势预测模型。首先利用鲸鱼群算法（WOA）优化选择变分模态分解（VMD）参数，避免盲目选择参数的弊端，实现振动信号的最优自适应分解，然后对分解后的每个分量信号进行归一化处理并建立TCN进行趋势预测，最后将所得结果叠加即可得到原振动信号预测结果。以国内某电站实际运行数据对所提模型进行论证试验，结果表明所提模型具有较高的预测精度，可用于工程实际。

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王璞（1985-），男，高级工程师，研究方向为常规水电及抽水蓄能运行维护管理，E-mail：67699531@qq.com

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王璞（1985-），男，高级工程师，研究方向为常规水电及抽水蓄能运行维护管理，E-mail：67699531@qq.com

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