RESEARCH ON DC ARC FAULT DETECTION OF PV SYSTEM BASED ON Kmeans++ CLUSTERING

Wu Zhou; Zhang Jun; Li Long; Li Baolong; Chen Hui

doi:10.19912/j.0254-0096.tynxb.2023-1137

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 320-329. DOI: 10.19912/j.0254-0096.tynxb.2023-1137

RESEARCH ON DC ARC FAULT DETECTION OF PV SYSTEM BASED ON Kmeans++ CLUSTERING

Wu Zhou¹, Zhang Jun², Li Long³, Li Baolong¹, Chen Hui¹

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Abstract

A fault detection method based on the combination of complementary set empirical mode decomposition （CEEMD） and K-means clustering （Kmeans++） is proposed to address the issue of difficult identification of DC side series arc faults in photovoltaic systems due to weak signals and strong randomness. Firstly, the DC side current signal of the photovoltaic system is decomposed into several intrinsic mode functions （IMF） using CEEMD, and then Pearson correlation coefficients are used to filter out effective modal components for signal reconstruction. Secondly, extract time-frequency domain features of the reconstructed signal and apply Kmeans++for fault identification. The experimental results show that the proposed method can effectively detect faults. Given the complexity of actual photovoltaic system operation, the influence of different external disturbances on arc detection algorithms was studied, and the superiority of this method in anti-interference was verified through experimental data. Finally, the proposed method was compared with fault detection methods based on PNN and SVM, and experimental data was used to verify its good fault recognition accuracy.

Key words

photovoltaic effects / electric arcs / fault detection / mode decomposition / feature extraction / cluster analysis

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Wu Zhou, Zhang Jun, Li Long, Li Baolong, Chen Hui. RESEARCH ON DC ARC FAULT DETECTION OF PV SYSTEM BASED ON Kmeans++ CLUSTERING[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 320-329 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1137

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