Arc Fault Detection Algorithm Based on Variational Mode Decomposition and Improved Multi-Scale Fuzzy Entropy

Lina Wang, Hongcheng Qiu, Pu Yang and Longhua Mu
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Lina Wang: School of Automation Science and Electrical Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China
Hongcheng Qiu: School of Automation Science and Electrical Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China
Pu Yang: School of Automation Science and Electrical Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China
Longhua Mu: Department of Electrical Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, China

Energies, 2021, vol. 14, issue 14, 1-16

Abstract: Arc fault diagnosis is necessary for the safety and efficiency of PV stations. This study proposed an arc fault diagnosis algorithm formed by combining variational mode decomposition (VMD), improved multi-scale fuzzy entropy (IMFE), and support vector machine (SVM). This method first uses VMD to decompose the current into intrinsic mode functions (IMFs) in the time-frequency domain, then calculates the IMFE according to the IMFs associated with the arc fault. Finally, it uses SVM to detect arc faults according to IMFEs. Arc fault data gathered from a PV arc generation experiment platform are used to validate the proposed method. The results indicated the proposed method can classify arc fault data and normal data effectively.

Keywords: arc fault diagnosis; variational mode decomposition; multi-scale fuzzy entropy; intrinsic mode functions; support vector machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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