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Photovoltaic Array Fault Detection by Automatic Reconfiguration

Author

Listed:
  • Dong Ji

    (School of Computer Science and Engineering, Northeastern University, Shenyang 116024, China
    Current address: No. 195, Chuangxin Road, Hunnan District, Shenyang 116024, China)

  • Cai Zhang

    (School of Computer Science and Engineering, Northeastern University, Shenyang 116024, China
    Current address: No. 195, Chuangxin Road, Hunnan District, Shenyang 116024, China)

  • Mingsong Lv

    (School of Computer Science and Engineering, Northeastern University, Shenyang 116024, China
    Current address: No. 195, Chuangxin Road, Hunnan District, Shenyang 116024, China)

  • Ye Ma

    (School of Computer Science and Engineering, Northeastern University, Shenyang 116024, China
    Current address: No. 195, Chuangxin Road, Hunnan District, Shenyang 116024, China)

  • Nan Guan

    (School of Computer Science and Engineering, Northeastern University, Shenyang 116024, China
    Current address: No. 195, Chuangxin Road, Hunnan District, Shenyang 116024, China)

Abstract

Photovoltaic (PV) system output electricity is related to PV cells’ conditions, with the PV faults decreasing the efficiency of the PV system and even causing a possible source of fire. In industrial production, PV fault detection is typically laborious manual work. In this paper, we present a method that can automatically detect PV faults. Based on the observation that different faults will have different impacts on a PV system, we propose a method that systematically and iteratively reconfigures the PV array until the faults are located based on the specific current-voltage (I-V) curve of the (sub-)array. Our method can detect several main types of faults including open-circuit faults, mismatch faults, short circuit faults, etc. We evaluate our methods by Matlab/Simulink-based simulation. The results show that the proposed methods can accurately detect and classify the different faults occurring in a PV system.

Suggested Citation

  • Dong Ji & Cai Zhang & Mingsong Lv & Ye Ma & Nan Guan, 2017. "Photovoltaic Array Fault Detection by Automatic Reconfiguration," Energies, MDPI, vol. 10(5), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:699-:d:98768
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    References listed on IDEAS

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    Cited by:

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    4. Luis D. Murillo-Soto & Carlos Meza, 2021. "Automated Fault Management System in a Photovoltaic Array: A Reconfiguration-Based Approach," Energies, MDPI, vol. 14(9), pages 1-19, April.
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    6. Rico Espinosa, Alejandro & Bressan, Michael & Giraldo, Luis Felipe, 2020. "Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 249-256.
    7. Lisa B. Bosman & Walter D. Leon-Salas & William Hutzel & Esteban A. Soto, 2020. "PV System Predictive Maintenance: Challenges, Current Approaches, and Opportunities," Energies, MDPI, vol. 13(6), pages 1-16, March.
    8. Ahmed Al Mansur & Md. Ruhul Amin & Kazi Khairul Islam, 2019. "Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations," Energies, MDPI, vol. 12(5), pages 1-21, March.
    9. Gabriella-Stefánia Szabó & Róbert Szabó & Loránd Szabó, 2022. "A Review of the Mitigating Methods against the Energy Conversion Decrease in Solar Panels," Energies, MDPI, vol. 15(18), pages 1-21, September.
    10. Lin, Wenye & Ma, Zhenjun & Li, Kehua & Tyagi, V.V. & Pandey, A.K., 2021. "A dynamic simulation platform for fault modelling and characterisation of building integrated photovoltaics," Renewable Energy, Elsevier, vol. 179(C), pages 963-981.

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