Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems

Lina Wang, Ehtisham Lodhi, Pu Yang, Hongcheng Qiu, Waheed Ur Rehman, Zeeshan Lodhi, Tariku Sinshaw Tamir and M. Adil Khan
Additional contact information
Lina Wang: School of Automation Science and Electrical Engineering, Beihang University, Xueyuan Road No. 37, Beijing 100191, China
Ehtisham Lodhi: School of Automation Science and Electrical Engineering, Beihang University, Xueyuan Road No. 37, Beijing 100191, China
Pu Yang: School of Automation Science and Electrical Engineering, Beihang University, Xueyuan Road No. 37, Beijing 100191, China
Hongcheng Qiu: School of Automation Science and Electrical Engineering, Beihang University, Xueyuan Road No. 37, Beijing 100191, China
Waheed Ur Rehman: Department of Mechanical Engineering, National University of Technology, Islamabad 44000, Pakistan
Zeeshan Lodhi: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
Tariku Sinshaw Tamir: The SKL for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
M. Adil Khan: Department of Computer and Technology, Chang’an University, Xi’an 710062, China

Energies, 2022, vol. 15, issue 10, 1-16

Abstract: DC series arc fault detection is essential for improving the productivity of photovoltaic (PV) stations. The DC series arc fault also poses severe fire hazards to the solar equipment and surrounding building. DC series arc faults must be detected early to provide reliable and safe power delivery while preventing fire hazards. However, it is challenging to detect DC series arc faults using conventional overcurrent and current differential methods because these faults produce only minor current variations. Furthermore, it is hard to define their characteristics for detection due to the randomness of DC arc faults and other arc-like transients. This paper focuses on investigating a novel method to extract arc characteristics for reliably detecting DC series arc faults in PV systems. This methodology first uses an adaptive local mean decomposition (ALMD) algorithm to decompose the current samples into production functions ( PF s) representing information from different frequency bands, then selects the PF s that best characterize the arc fault, and then calculates its multiscale fuzzy entropies (MFEs). Eventually, MFE values are inputted to the trained SVM algorithm to identify the series arc fault accurately. Furthermore, the proposed technique is compared to the logistic regression algorithm and naive Bayes algorithm in terms of several metrics assessing algorithms’ validity for detecting arc faults in PV systems. Arc fault data acquired from a PV arc-generating experiment platform are utilized to authenticate the effectiveness and feasibility of the proposed method. The experimental results indicated that the proposed technique could efficiently classify the arc fault data and normal data and detect the DC series arc faults in less than 1 ms with an accuracy rate of 98.75%.

Keywords: series arc; photovoltaic (PV); adaptive local mean decomposition (ALMD); multiscale fuzzy entropy (MFE); support vector machine (SVM) (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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