Article(id=1222499169378492909, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222499161174434087, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202212233, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1670860800000, revisedDateStr=2022-12-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1769396114915, onlineDateStr=2026-01-26, pubDate=1695571200000, pubDateStr=2023-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769396114915, onlineIssueDateStr=2026-01-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769396114915, creator=13701087609, updateTime=1769396114915, updator=13701087609, issue=Issue{id=1222499161174434087, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='9', pageStart='1', pageEnd='204', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769396112960, creator=13701087609, updateTime=1769396581892, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1222501128068129514, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222499161174434087, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1222501128068129515, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222499161174434087, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=162, endPage=170, ext={EN=ArticleExt(id=1222499169785340426, articleId=1222499169378492909, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Joint probabilistic risk assessment of wind power transformer connected to distribution networks considering source-load correlation, columnId=1211002409397129992, journalTitle=Thermal Power Generation, columnName=Power generation technology forum, runingTitle=null, highlight=null, articleAbstract=

With the increasing penetration of new energy in new power system, the influence of the uncertainty of wind power output and the correlation with load on the system operation is increasingly prominent. Thus, the conventional transformer risk assessment can no longer meet the demand. Based on Copula model and Susa model, a joint probability assessment method of transformer operation risk considering wind power and load correlation is proposed. Monte Carlo method is used to calculate each risk index value and evaluate transformer operation risk. The research results show that, the conventional transformer evaluation system that does not consider the wind-load correlation will lead to a low overall risk assessment result, with the maximum error of the index up to 55.02%. Also, with the increase of wind-load correlation, the risk of thermal defects of the transformer is increasing. The research conclusions can assist to improve the accuracy of the operation risk level assessment of new power system transformers, and provide a reference for the later transformer condition assessment and maintenance plan formulation.

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高比例新能源的逐步接入改变了电力系统的运行方式，新能源接入配网侧的变压器运行环境也发生了极大改变，基于传统电力系统“源随荷动”的变压器风险评估体系已无法满足现有新型电力系统“源荷互动”的评估需求。基于Copula模型与变压器Susa模型，建立风电接入的新能源配电网中“源”“荷”新型相关性模型，并引入绝缘劣化概率等指标，提出了一种考虑风电-负荷相关性的变压器运行风险联合概率评估方法，利用蒙特卡罗法计算各个风险指标值并对变压器运行风险进行评估。研究结果表明，传统不考虑“源”“荷”相关性的变压器评估体系会导致整体风险评估结果偏低，指标最高误差可达55.02%，且随着相关性提升，变压器热缺陷风险不断增加。研究结论可辅助提升新型电力系统变压器运行风险等级评估准确度，为今后变压器状态评估与检修计划制定提供参考。

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甘艳（1976），女，博士，教授级高级工程师，主要研究方向为智能电网输变电设备在线监测理论与关键技术，gany@cc.sgcc.com.cn。

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甘艳（1976），女，博士，教授级高级工程师，主要研究方向为智能电网输变电设备在线监测理论与关键技术，gany@cc.sgcc.com.cn。

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