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

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.

Classification and Parametric Analysis of Solar Hybrid PVT System: A Review

“…Through a comprehensive analysis of several variables, including geometric, geographical, meteorological, external/climatic, and internal aspects, the paper proposes a design and optimization roadmap for hybrid PVT systems. This strategy is essential for the implementation of practical PVT systems, as it may improve performance and overall efficiency under various environmental [96] circumstances.…”

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

“…The results indicate that the proposed approach correctly classifies defects with an overall accuracy of 99% [33]. In the literature, several articles have widely discussed several aspects of machine learning in fault diagnosis by highlighting the models used, their advantages and disadvantages, the parameters studied and the results obtained [34][35][36][37]. For instance, a systematic review of the use of Artificial Intelligence (AI) techniques in photovoltaic (PV) fault diagnosis and identification revealed the significant role of AI in image analysis, anomaly detection, and optimization.…”

“…For instance, a systematic review of the use of Artificial Intelligence (AI) techniques in photovoltaic (PV) fault diagnosis and identification revealed the significant role of AI in image analysis, anomaly detection, and optimization. The authors concentrate on AI techniques such as Machine Learning, Deep Learning, Machine Vision, and Natural Language Processing (NLP) [37]. An analysis of the reviews employed in this paper indicates that machine learning techniques are extensively utilized in the diagnosis of PV systems.…”

“…This paper examines the types of faults in PV systems, their causes and consequences, and presents the most popular supervised learning methods for PV fault diagnosis in recent years. It also summarizes the reviews published on Artificial Intelligence methods for PV fault diagnosis from 2016 to 2023 [37,[44][45][46][47][48][49][50][51][52]. Finally, the Extra Trees algorithm is proposed as a new robust classifier capable of improving the inadequacies of other classifiers in the fault diagnosis of photovoltaic (PV) systems.…”

Using machine learning for the optimisation of operations and management in electric systems and networks

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