PHOTOVOLTAIC MODULE AGING FAULT DIAGNOSIS METHOD BASED ON TIME SERIES FEATURE EXTRACTION

被引：0
作者：
He, Yunxiao [1 ]
Wei, Dong [2 ]
Guo, Qian [1 ]
Gu, Xinlei [1 ]
机构：
[1] College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou
[2] College of Modern Science and Technology, China Jiliang University, Hangzhou
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 11期
关键词：
aging; fault diagnosis; feature extraction; fuzzy C-means clustering; photovoltaic; time series;
D O I：
10.19912/j.0254-0096.tynxb.2023-1049
中图分类号：
学科分类号：
摘要：
By analyzing the generation and evolution mechanisms of aging faults and comparing the differences in time-series characteristics among thermal hotspots，micro-cracks，and potential-induced degradation（PID）faults，the unique variation patterns of aging faults in time-series data are determined. By calculating the parameters of the equivalent circuit model of PV string arrays，the influence and correlation of these variation patterns on the model parameters are studied and verified，resulting in the identification of aging fault diagnosis feature vectors. A fuzzy C-means clustering algorithm is employed to propose an aging fault diagnosis method based on time-series feature extraction for PV. Simulation and experimental results demonstrate that the computed model parameters effectively describe the characteristic variations in the time-series data. The identified fault diagnosis features effectively represent the occurrence and evolution processes of aging faults. The proposed fault diagnosis method reliably achieves cause determination，severity classification，and severity estimation of aging faults in PV systems. © 2024 Science Press. All rights reserved.
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页码：204 / 211
页数：7
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