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Application of Artificial Intelligence in PV Fault Detection

Author

Listed:
  • Ahmed A. Al-Katheri

    (Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
    K.A.CARE Energy Research and Innovation Center, King Saud University, Riyadh 11421, Saudi Arabia)

  • Essam A. Al-Ammar

    (Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
    K.A.CARE Energy Research and Innovation Center, King Saud University, Riyadh 11421, Saudi Arabia)

  • Majed A. Alotaibi

    (Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
    K.A.CARE Energy Research and Innovation Center, King Saud University, Riyadh 11421, Saudi Arabia)

  • Wonsuk Ko

    (Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Sisam Park

    (GS E&C Institute, GS E&C Corp., 33 Jong-ro, Jongno-gu, Seoul 03159, Korea)

  • Hyeong-Jin Choi

    (GS E&C Institute, GS E&C Corp., 33 Jong-ro, Jongno-gu, Seoul 03159, Korea)

Abstract

The rapid revolution in the solar industry over the last several years has increased the significance of photovoltaic (PV) systems. Power photovoltaic generation systems work in various outdoor climate conditions; therefore, faults may occur within the PV arrays in the power system. Fault detection is a fundamental task needed to improve the reliability, efficiency, and safety of PV systems, and, if not detected, the cost associated with the loss of power generated from PV modules will be quite high. Moreover, maintenance staff will take more time and effort to fix undetermined faults. Due to the current-limiting nature and nonlinear output characteristics of PV arrays, fault detection is not that easy and the application of artificial intelligence is proposed for the sake of fault detection in PV systems. The idea behind this approach is to compare the faulty PV module with its accurate model (factory fingerprint) by checking every PV array’s I–V and P–V curves using the Artificial Neural Network (ANN) logarithm as a subsection of the Artificial Intelligence’s (AI) techniques. This proposed approach achieves a high performance of fault detection and gives the advantage of determining what type of fault has occurred. The results confirm that the proposed logarithm performance becomes better as the number of distinguishing points extend, providing great value to the Solar PV industry.

Suggested Citation

  • Ahmed A. Al-Katheri & Essam A. Al-Ammar & Majed A. Alotaibi & Wonsuk Ko & Sisam Park & Hyeong-Jin Choi, 2022. "Application of Artificial Intelligence in PV Fault Detection," Sustainability, MDPI, vol. 14(21), pages 1-25, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13815-:d:952208
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    References listed on IDEAS

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    1. Dhimish, Mahmoud & Holmes, Violeta & Mehrdadi, Bruce & Dales, Mark & Mather, Peter, 2017. "Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system," Energy, Elsevier, vol. 140(P1), pages 276-290.
    2. Chen, Zhicong & Wu, Lijun & Cheng, Shuying & Lin, Peijie & Wu, Yue & Lin, Wencheng, 2017. "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Applied Energy, Elsevier, vol. 204(C), pages 912-931.
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