DC SERIES ARC FAULT FEATURE EXTRACTION FOR PHOTOVOLTAIC SYSTEM BASED ON DYNAMIC TIME WARPING

Li Xin; Gao Wei; Yang Gengjie

doi:10.19912/j.0254-0096.tynxb.2022-1332

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (12) : 82-89. DOI: 10.19912/j.0254-0096.tynxb.2022-1332

DC SERIES ARC FAULT FEATURE EXTRACTION FOR PHOTOVOLTAIC SYSTEM BASED ON DYNAMIC TIME WARPING

Li Xin, Gao Wei, Yang Gengjie

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Abstract

In light of challenges encountered in extracting DC series arc fault features within photovoltaic （PV） system and the observed limitations in algorithm generalization and adaptability, this study introduces a novel series arc fault feature extraction method based on dynamic time warping （DTW）. Initially, the moving average （MA） value of the current signal is calculated to identify the mutation event and collect the abnormal signal. Then, the singular spectrum analysis （SSA） is used to remove the trend component of abnormal signals and reduce the differences between different PV system signals. Following this, the DTW distance of the signal is calculated to extract the valid features. In the end, the waveform factor of the identified feature vector serves as the diagnostic criterion to identify the arc fault, short circuit fault and the interference events caused by inverter start-up and irradiance mutation. The experimental results show that the arc fault identification method based on the proposed feature extraction is not only fast and highly recognizable, but also suitable for different inverter systems, has strong adaptability, and the comprehensive performance is better than that of the comparison method.

Key words

photovoltaic system / electric arcs / fault detection / dynamic time warping（DTW） / singular spectrum analysis（SSA）

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Li Xin, Gao Wei, Yang Gengjie. DC SERIES ARC FAULT FEATURE EXTRACTION FOR PHOTOVOLTAIC SYSTEM BASED ON DYNAMIC TIME WARPING[J]. Acta Energiae Solaris Sinica. 2023, 44(12): 82-89 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1332

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