Article(id=1222493254046699772, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222493244286558340, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202212198, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1670256000000, receivedDateStr=2022-12-06, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769394704591, onlineDateStr=2026-01-26, pubDate=1692892800000, pubDateStr=2023-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769394704591, onlineIssueDateStr=2026-01-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769394704590, creator=13701087609, updateTime=1769394704590, updator=13701087609, issue=Issue{id=1222493244286558340, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='8', pageStart='1', pageEnd='196', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769394702264, creator=13701087609, updateTime=1769394819736, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1222493737050169898, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222493244286558340, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1222493737050169899, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222493244286558340, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=188, endPage=196, ext={EN=ArticleExt(id=1222493254373855499, articleId=1222493254046699772, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Research on equipment health of distributed energy power station based on improved Mahalanobis distance, columnId=1211002409397129992, journalTitle=Thermal Power Generation, columnName=Power generation technology forum, runingTitle=null, highlight=null, articleAbstract=

Distributed energy power stations are developing rapidly because of their cleanliness, environmental protection, economy and high efficiency. However, there are few data used for fault diagnosis of plant equipment, so a method to predict the health state and aging degree of equipment is urgently needed. Based on this, a prediction model which can analyze the running state of equipment and obtain the deterioration trend of equipment is proposed. Firstly, multi-dimensional data of the equipment is preprocessed, and an improved Mahalanobis distance based equipment health model of distributed energy power station is constructed quantitatively by combining the analytic hierarchy process (AHP) with Gaussian mixture distribution. Then, the combined prediction model based on the improved sparrow algorithm and short and long time memory neural network is established to predict the trend and correlation analysis of the deterioration of distributed energy power plant equipment. The experimental results show that the proposed fusion health model can predict equipment anomalies in the case of insufficient actual fault data of distributed energy power stations.

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分布式能源电站以其清洁环保、经济高效得到快速发展，但其电站设备用于故障诊断的数据较少，目前急需一种能够预测设备运行健康状态和老化程度的方法。基于此，提出一种能够分析设备运行状态、获取设备劣化演变趋势的预测模型。首先将设备多维数据进行预处理，采用层次分析法与高斯混合分布相结合，定量地构建一种基于改进马氏距离的分布式能源电站设备健康度模型；然后建立基于改进麻雀算法和长短时记忆神经网络的组合预测模型，对分布式能源电站设备的劣化情况做趋势预测及相关分析。实验结果表明，所提出的融合健康度模型在分布式能源电站实际故障数据不足的情况下，能够在设备出现异常时做出预测。

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陈子洋（1996），男，硕士研究生，主要研究方向为智能发电、设备故障诊断，13726936261@163.com。

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陈子洋（1996），男，硕士研究生，主要研究方向为智能发电、设备故障诊断，13726936261@163.com。

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