Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Mahmudul Islam, Masud Rana Rashel (), Md Tofael Ahmed, A. K. M. Kamrul Islam and Mouhaydine Tlemçani
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Mahmudul Islam: Department of Computer Science and Engineering, Independent University, Dhaka 1229, Bangladesh
Masud Rana Rashel: Instrumentation and Control Laboratory, Department of Mechatronics Engineering, University of Évora, 7000-671 Évora, Portugal
Md Tofael Ahmed: Instrumentation and Control Laboratory, Department of Mechatronics Engineering, University of Évora, 7000-671 Évora, Portugal
A. K. M. Kamrul Islam: College of Engineering, North Carolina A&T State University, Greensboro, NC 27411, USA
Mouhaydine Tlemçani: Instrumentation and Control Laboratory, Department of Mechatronics Engineering, University of Évora, 7000-671 Évora, Portugal

Energies, 2023, vol. 16, issue 21, 1-18

Abstract: Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification.

Keywords: photovoltaic fault; Artificial Intelligence; machine learning; deep learning; Artificial Neural Network; Convolutional Neural Network; Recurrent Neural Network; computer vision; unmanned aerial vehicles; systematic review (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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