AUTHOR=Ghaedi Peyman , Eskandari Aref , Nedaei Amir , Hatami Farzad , Parvin Parviz , Aghaei Mohammadreza 

TITLE=Logically optimized and probabilistic integrated photovoltaic fault finding package based on machine learning

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2025.1675953

DOI=10.3389/fenrg.2025.1675953

ISSN=2296-598X

ABSTRACT=IntroductionArtificial intelligence (AI) has been widely used to detect faults and failures in photovoltaic (PV) systems, particularly those that conventional protection devices fail to identify. However, previous AI-based approaches still face major limitations, including neglecting critical detection conditions, relying on large and complex datasets, and lacking simultaneous and accurate multi-fault detection and classification.MethodsTo address these challenges, a novel PV fault detection framework is proposed by combining a fuzzy logic (FL) system with a particle swarm optimization (PSO) algorithm. An initial dataset is generated from the current–voltage (I–V) curve of a PV array. Manhattan distance (MD) and Chebyshev distance (CD) features are extracted from the I–V characteristics. A wide set of machine-learning classifiers is evaluated, and the FL system nominates the most reliable models based on mean accuracy, F1-score, and standard deviation. PSO is then used to determine the optimal subset of classifiers and to assign optimized weights for ensemble prediction. Several output-combining techniques are also examined to obtain the most accurate final classification.ResultsModel verification is performed using a dataset that includes normal operation as well as line-to-line (LL), open-circuit (OC), and degradation (DEG) faults under various environmental (irradiance, temperature) and electrical (mismatch, impedance) conditions. The proposed FL+PSO-based model achieves outstanding accuracy in detecting and classifying multiple PV faults and outperforms recent state-of-the-art approaches.DiscussionThe integration of distance-based feature extraction, fuzzy-driven classifier selection, and PSO-optimized weighting significantly enhances robustness and reduces sensitivity to environmental variations. These improvements enable reliable multi-fault detection even when fault signatures closely resemble normal conditions.ConclusionThe proposed FL and PSO-based ensemble provides a highly accurate and reliable solution for multi-fault detection in PV arrays. Its performance surpasses existing approaches, making it a strong candidate for practical implementation in real PV monitoring systems.