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Article(id=1322532017359671487, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1322531779840426416, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.7.147, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751212800000, receivedDateStr=2025-06-30, revisedDate=1757260800000, revisedDateStr=2025-09-08, acceptedDate=1758038400000, acceptedDateStr=2025-09-17, onlineDate=1793245804394, onlineDateStr=2026-10-29, pubDate=1785340800000, pubDateStr=2026-07-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793245804394, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793245804394, creator=13701087609, updateTime=1793245804394, updator=13701087609, issue=Issue{id=1322531779840426416, tenantId=1146029695717560320, journalId=1146031654075715584, year='2026', volume='24', issue='7', pageStart='1', pageEnd='368', issueExtLink='null', onlineDate='null', pubDate='1785340800000', pubDateStr='2026-07-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793245747766, creator='13701087609', updateTime=1793245747766, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=147, endPage=162, ext={EN=ArticleExt(id=1322532017535832256, articleId=1322532017359671487, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Distributed Photovoltaic Anomaly Data Identification Method Considering Time Series Clustering, columnId=1321491614980223555, journalTitle=Journal of Power Supply, columnName=Renewable Energy Generation and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

In order to address the shortcomings of existing detection methods, which are unable to effectively capture the complex nonlinear features of photovolatic time series data, a distributed photovolatic anomaly data detection method considering time series clustering is proposed. This method is designed to facilitate the accurate identification and location of abnormal data. Firstly, the similarity of photovolatic power plant time series data is calculated using the dynamic time warping (DTW) method, and a similarity matrix is constructed. Subsequently, spectral clustering is applied to obtain the feature matrix, and the intrinsic connection between photovolatic data points in the feature matrix is analysed. This enables similar photovolatic data to be clustered into a collection. Then, a joint detection model of sub-isolated forest is established based on the collection of photovolatic data clusters. The abnormal score geatures are obtained using the local outlier factor algorithm. The features are used as inputs to the joint detection model to obtain the anomaly score matrix of photovolatic data for each similar clustering set. Finally, the anomaly score matrix is selected and reconstructed, and the anomalies of photovolatic data are identified and located after decision analysis. The experimental results demonstrate that the method is an effective means of capturing photovolatic anomaly data. The precision and recall rates of extracted anomaly samples reach 0.9, thereby enhancing the accuracy of photovolatic anomaly data identification.

, authors=Xiaoran BI1, Liang WANG1, Hongjie LIANG2, Shuai ZUO1, Binhong XU1, Zihan WANG1, authorsList=Xiaoran BI, Liang WANG, Hongjie LIANG, Shuai ZUO, Binhong XU, Zihan WANG, authorCompany=null, correspAuthors=Liang WANG, 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=1322532018068508867, articleId=1322532017359671487, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=考虑时序聚类的分布式光伏异常数据辨识方法, columnId=1321491615269630533, journalTitle=电源学报, columnName=新能源发电与储能, runingTitle=null, highlight=null, articleAbstract=

针对现有检测方法忽视或无法有效捕捉光伏时序数据复杂长期非线性特征，导致异常数据难以准确识别和定位的问题，提出了考虑时序聚类的分布式光伏异常数据检测方法。首先，采用动态时间规整DTW（dynamic time warping）方法计算光伏电站时序数据的相似度，构建相似度矩阵；接着，在此矩阵基础上应用谱聚类得到特征矩阵，分析特征矩阵光伏数据点间的内在联系，将相似光伏数据聚类成集合；然后，建立基于光伏数据聚类集合的子孤立森林联合检测模型，采用局部离群因子算法获得异常分数特征，作为联合检测模型输入，得到各相似聚类集合的光伏数据异常分数矩阵；最后，对异常分数矩阵进行选择重构，经过决策分析判别并定位光伏数据的异常。实验结果表明，该方法可以有效捕获光伏异常数据，提取异常样本精准率与召回率均可达到0.9，提高了光伏异常数据识别的准确性。

, authors=毕校然1, 王亮1, 梁宏杰2, 左帅1, 徐彬红1, 王梓涵1, authorsList=毕校然, 王亮, 梁宏杰, 左帅, 徐彬红, 王梓涵, authorCompany=null, correspAuthors=王亮, authorNote=

毕校然（1999—　），男，硕士研究生。研究方向：电力设备在线监测与故障诊断。E-mail：。

梁宏杰（1997—　），男，硕士研究生。研究方向：电力设备在线监测与故障诊断。E-mail：。

左帅（2002—　），男，硕士研究生。研究方向：光伏阵列。E-mail：。

徐彬红（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：。

王梓涵（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：。

, correspAuthorsNote=
王亮（1983—　），男，通信作者，博士，副教授。研究方向：电力设备在线监测与故障诊断。E-mail：。
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毕校然（1999—　），男，硕士研究生。研究方向：电力设备在线监测与故障诊断。E-mail：。

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毕校然（1999—　），男，硕士研究生。研究方向：电力设备在线监测与故障诊断。E-mail：。

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梁宏杰（1997—　），男，硕士研究生。研究方向：电力设备在线监测与故障诊断。E-mail：。

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梁宏杰（1997—　），男，硕士研究生。研究方向：电力设备在线监测与故障诊断。E-mail：。

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左帅（2002—　），男，硕士研究生。研究方向：光伏阵列。E-mail：。

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左帅（2002—　），男，硕士研究生。研究方向：光伏阵列。E-mail：。

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徐彬红（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：。

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徐彬红（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：。

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王梓涵（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：。

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王梓涵（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：。

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毕校然 1 , 王亮 1 , 梁宏杰 2 , 左帅 1 , 徐彬红 1 , 王梓涵 1
电源学报 | 新能源发电与储能 2026,24(7): 147-162
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电源学报 |新能源发电与储能 2026 , 24 (7) : 147 -162
考虑时序聚类的分布式光伏异常数据辨识方法
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毕校然（1999—　），男，硕士研究生。研究方向：电力设备在线监测与故障诊断。E-mail：。

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毕校然（1999—　），男，硕士研究生。研究方向：电力设备在线监测与故障诊断。E-mail：。

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梁宏杰（1997—　），男，硕士研究生。研究方向：电力设备在线监测与故障诊断。E-mail：。

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左帅（2002—　），男，硕士研究生。研究方向：光伏阵列。E-mail：。

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徐彬红（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：。

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王梓涵（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：。

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毕校然1 , 王亮1 , 梁宏杰2 , 左帅1 , 徐彬红1 , 王梓涵1
作者信息
  • 1.沈阳工程学院电气工程学院，沈阳　110136
  • 2.国网甘肃省电力公司超高压公司，兰州　730030
通讯作者:
王亮（1983—　），男，通信作者，博士，副教授。研究方向：电力设备在线监测与故障诊断。E-mail：。
作者简介:

毕校然（1999—　），男，硕士研究生。研究方向：电力设备在线监测与故障诊断。E-mail：。

梁宏杰（1997—　），男，硕士研究生。研究方向：电力设备在线监测与故障诊断。E-mail：。

左帅（2002—　），男，硕士研究生。研究方向：光伏阵列。E-mail：。

徐彬红（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：。

王梓涵（2000—　），男，硕士研究生。研究方向：智能优化算法。E-mail：。

Distributed Photovoltaic Anomaly Data Identification Method Considering Time Series Clustering
Xiaoran BI1 , Liang WANG1 , Hongjie LIANG2 , Shuai ZUO1 , Binhong XU1 , Zihan WANG1
Affiliations
  • 1.School of Electrical Engineering, Shenyang Institute of Engineering, Shenyang 110136, China
  • 2.Ultra-high Voltage Company of State Grid Gansu Electric Power Co., Ltd., Lanzhou 730030, China
出版时间: 2026-07-30 doi: 10.13234/j.issn.2095-2805.2026.7.147
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针对现有检测方法忽视或无法有效捕捉光伏时序数据复杂长期非线性特征，导致异常数据难以准确识别和定位的问题，提出了考虑时序聚类的分布式光伏异常数据检测方法。首先，采用动态时间规整DTW（dynamic time warping）方法计算光伏电站时序数据的相似度，构建相似度矩阵；接着，在此矩阵基础上应用谱聚类得到特征矩阵，分析特征矩阵光伏数据点间的内在联系，将相似光伏数据聚类成集合；然后，建立基于光伏数据聚类集合的子孤立森林联合检测模型，采用局部离群因子算法获得异常分数特征，作为联合检测模型输入，得到各相似聚类集合的光伏数据异常分数矩阵；最后，对异常分数矩阵进行选择重构，经过决策分析判别并定位光伏数据的异常。实验结果表明，该方法可以有效捕获光伏异常数据，提取异常样本精准率与召回率均可达到0.9，提高了光伏异常数据识别的准确性。

异常辨识  /  聚类算法  /  分布式电源  /  子孤立森林框架  /  光伏数据异常  /  长期趋势验证

In order to address the shortcomings of existing detection methods, which are unable to effectively capture the complex nonlinear features of photovolatic time series data, a distributed photovolatic anomaly data detection method considering time series clustering is proposed. This method is designed to facilitate the accurate identification and location of abnormal data. Firstly, the similarity of photovolatic power plant time series data is calculated using the dynamic time warping (DTW) method, and a similarity matrix is constructed. Subsequently, spectral clustering is applied to obtain the feature matrix, and the intrinsic connection between photovolatic data points in the feature matrix is analysed. This enables similar photovolatic data to be clustered into a collection. Then, a joint detection model of sub-isolated forest is established based on the collection of photovolatic data clusters. The abnormal score geatures are obtained using the local outlier factor algorithm. The features are used as inputs to the joint detection model to obtain the anomaly score matrix of photovolatic data for each similar clustering set. Finally, the anomaly score matrix is selected and reconstructed, and the anomalies of photovolatic data are identified and located after decision analysis. The experimental results demonstrate that the method is an effective means of capturing photovolatic anomaly data. The precision and recall rates of extracted anomaly samples reach 0.9, thereby enhancing the accuracy of photovolatic anomaly data identification.

Anomaly identification  /  clustering algorithms  /  distributed generation  /  sub-isolated forest framework  /  photovolatic data anomalies  /  long-term trend verification
毕校然, 王亮, 梁宏杰, 左帅, 徐彬红, 王梓涵. 考虑时序聚类的分布式光伏异常数据辨识方法. 电源学报, 2026 , 24 (7) : 147 -162 . DOI: 10.13234/j.issn.2095-2805.2026.7.147
Xiaoran BI, Liang WANG, Hongjie LIANG, Shuai ZUO, Binhong XU, Zihan WANG. Distributed Photovoltaic Anomaly Data Identification Method Considering Time Series Clustering[J]. Journal of Power Supply, 2026 , 24 (7) : 147 -162 . DOI: 10.13234/j.issn.2095-2805.2026.7.147
基金
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  • 新型电力系统下的分布式光伏网络安全防护方法研究资助项目(SGLNJZ00HUS2311442)
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2026年第24卷第7期
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doi: 10.13234/j.issn.2095-2805.2026.7.147
  • 接收时间：2025-06-30
  • 首发时间：2026-10-29
  • 出版时间：2026-07-30
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  • 收稿日期：2025-06-30
  • 修回日期：2025-09-08
  • 录用日期：2025-09-17
基金
Research on Distributed Photovoltaic Network Security Protection Methods under the New Power System(SGLNJZ00HUS2311442)
新型电力系统下的分布式光伏网络安全防护方法研究资助项目(SGLNJZ00HUS2311442)
作者信息
    1.沈阳工程学院电气工程学院，沈阳　110136
    2.国网甘肃省电力公司超高压公司，兰州　730030

通讯作者:

王亮（1983—　），男，通信作者，博士，副教授。研究方向：电力设备在线监测与故障诊断。E-mail：。
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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