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Distributed Photovoltaic Anomaly Data Identification Method Considering Time Series Clustering
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Xiaoran BI1, Liang WANG1, Hongjie LIANG2, Shuai ZUO1, Binhong XU1, Zihan WANG1
Journal of Power Supply | 2026, 24(7) : 147 - 162
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Journal of Power Supply | 2026, 24(7): 147-162
• Renewable Energy Generation and Energy Storage •
Distributed Photovoltaic Anomaly Data Identification Method Considering Time Series Clustering
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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
Published: 2026-07-30 doi: 10.13234/j.issn.2095-2805.2026.7.147
Outline
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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
  • Research on Distributed Photovoltaic Network Security Protection Methods under the New Power System(SGLNJZ00HUS2311442)
Year 2026 volume 24 Issue 7
29
4
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.7.147
  • Receive Date:2025-06-30
  • Online Date:2026-10-29
  • Published:2026-07-30
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History
  • Received:2025-06-30
  • Revised:2025-09-08
  • Accepted:2025-09-17
Funding
Research on Distributed Photovoltaic Network Security Protection Methods under the New Power System(SGLNJZ00HUS2311442)
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
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表12种不同金属材料的力学参数
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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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