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

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School of Automation Science and Electrical Engineering, Beihang University, Xueyuan Road No. 37, Beijing 100191, China
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The SKL for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Department of Mechanical Engineering, National University of Technology, Islamabad 44000, Pakistan
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Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
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School of Electrical and Computer Engineering, Institute of Technology, Debremarkos University, Debremarkos 269, Ethiopia
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Department of Computer and Technology, Chang’an University, Xi’an 710062, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Pedro Dinis Gaspar, Pedro Dinho da Silva and Luís C. Pires
Received: 24 March 2022 / Revised: 6 May 2022 / Accepted: 9 May 2022 / Published: 15 May 2022
(This article belongs to the Topic Solar Thermal Energy and Photovoltaic Systems)
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 (PFs) representing information from different frequency bands, then selects the PFs 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%. View Full-Text
Keywords: series arc; photovoltaic (PV); adaptive local mean decomposition (ALMD); multiscale fuzzy entropy (MFE); support vector machine (SVM) series arc; photovoltaic (PV); adaptive local mean decomposition (ALMD); multiscale fuzzy entropy (MFE); support vector machine (SVM)
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MDPI and ACS Style

Wang, L.; Lodhi, E.; Yang, P.; Qiu, H.; Rehman, W.U.; Lodhi, Z.; Tamir, T.S.; Khan, M.A. Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems. Energies 2022, 15, 3608. https://0-doi-org.brum.beds.ac.uk/10.3390/en15103608

AMA Style

Wang L, Lodhi E, Yang P, Qiu H, Rehman WU, Lodhi Z, Tamir TS, Khan MA. Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems. Energies. 2022; 15(10):3608. https://0-doi-org.brum.beds.ac.uk/10.3390/en15103608

Chicago/Turabian Style

Wang, Lina, Ehtisham Lodhi, Pu Yang, Hongcheng Qiu, Waheed U. Rehman, Zeeshan Lodhi, Tariku S. Tamir, and M. A. Khan. 2022. "Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems" Energies 15, no. 10: 3608. https://0-doi-org.brum.beds.ac.uk/10.3390/en15103608

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