Artificial-Intelligence-Based Detection of Defects and Faults in Photovoltaic Systems: A Survey

Ali Thakfan and Yasser Bin Salamah ()
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Ali Thakfan: Joint Master’s Program in Renewable Energy, Deanship of Graduate Studies, King Saud University, Riyadh 11473, Saudi Arabia
Yasser Bin Salamah: Department of Electrical Engineering, King Saud University, Riyadh 11421, Saudi Arabia

Energies, 2024, vol. 17, issue 19, 1-23

Abstract: The global shift towards sustainable energy has positioned photovoltaic (PV) systems as a critical component in the renewable energy landscape. However, maintaining the efficiency and longevity of these systems requires effective fault detection and diagnosis mechanisms. Traditional methods, relying on manual inspections and standard electrical measurements, have proven inadequate, especially for large-scale solar installations. The emergence of machine learning (ML) and deep learning (DL) has sparked significant interest in developing computational strategies to enhance the identification and classification of PV system faults. Despite these advancements, challenges remain, particularly due to the limited availability of public datasets for PV fault detection and the complexity of existing artificial-intelligence (AI)-based methods. This study distinguishes itself by proposing a novel AI-based approach that optimizes fault detection and classification in PV systems, addressing existing gaps in AI-driven fault detection, especially in terms of thermal imaging and current–voltage (I-V) curve analysis. This comprehensive survey identifies emerging trends in AI-driven PV fault detection, highlights the most advanced methodologies, and proposes a novel AI-based approach to enhance fault detection and classification capabilities. The findings aim to advance the state of technology in this field, offering insights into more efficient and practical solutions for PV system fault management.

Keywords: solar PV; defect detection; machine learning; thermal images; I-V curves; neural networks; SVM; random forest; decision trees; logistic regression; KNN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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