Application of Artificial Intelligence in PV Fault Detection

Ahmed A. Al-Katheri (), Essam A. Al-Ammar, Majed A. Alotaibi, Wonsuk Ko, Sisam Park and Hyeong-Jin Choi
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Ahmed A. Al-Katheri: Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Essam A. Al-Ammar: Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Majed A. Alotaibi: Electrical Engineering Department, Faculty of Engineering, 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

Sustainability, 2022, vol. 14, issue 21, 1-25

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.

Keywords: PV system; fault detection; artificial intelligence; artificial neural networks; P–V curves; I–V curves (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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