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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

Author

Listed:
  • 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
    The SKL for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, 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
    School of Electrical and Computer Engineering, Institute of Technology, Debremarkos University, Debremarkos 269, Ethiopia)

  • M. Adil Khan

    (Department of Computer and Technology, Chang’an University, Xi’an 710062, China)

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%.

Suggested Citation

  • Lina Wang & Ehtisham Lodhi & Pu Yang & Hongcheng Qiu & Waheed Ur Rehman & Zeeshan Lodhi & Tariku Sinshaw Tamir & M. Adil Khan, 2022. "Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems," Energies, MDPI, vol. 15(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3608-:d:815935
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    References listed on IDEAS

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    1. Lina Wang & Hongcheng Qiu & Pu Yang & Longhua Mu, 2021. "Arc Fault Detection Algorithm Based on Variational Mode Decomposition and Improved Multi-Scale Fuzzy Entropy," Energies, MDPI, vol. 14(14), pages 1-16, July.
    2. Teng Li & Zhijie Jiao & Lina Wang & Yong Mu, 2020. "A Method of DC Arc Detection in All-Electric Aircraft," Energies, MDPI, vol. 13(16), pages 1-14, August.
    3. Weiguo He & Deyang Yin & Kaifeng Zhang & Xiangwen Zhang & Jianyong Zheng, 2021. "Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model," Energies, MDPI, vol. 14(14), pages 1-17, July.
    4. Ehtisham Lodhi & Fei-Yue Wang & Gang Xiong & Ghulam Ali Mallah & Muhammad Yaqoob Javed & Tariku Sinshaw Tamir & David Wenzhong Gao, 2021. "A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems," Sustainability, MDPI, vol. 13(19), pages 1-27, September.
    5. Lu, Shibo & Phung, B.T. & Zhang, Daming, 2018. "A comprehensive review on DC arc faults and their diagnosis methods in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 88-98.
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    Cited by:

    1. Krzysztof Dowalla & Piotr Bilski & Robert Łukaszewski & Augustyn Wójcik & Ryszard Kowalik, 2022. "A Novel Method for Detection and Location of Series Arc Fault for Non-Intrusive Load Monitoring," Energies, MDPI, vol. 16(1), pages 1-23, December.

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