Timely fault detection in photovoltaic systems is critical for ensuring energy efficiency,reliability,and cost-effectiveness.However,the nonlinear and weather-dependent behavior of photovoltaic systems poses challenge...Timely fault detection in photovoltaic systems is critical for ensuring energy efficiency,reliability,and cost-effectiveness.However,the nonlinear and weather-dependent behavior of photovoltaic systems poses challenges for accurate diagnosis.This study presents a large-scale review of 983 scientific publications on artificial intelligence-based photovoltaic fault detection,using a novel methodology called Topic-tSNE Fusion.This approach integrates topic modeling,dimensionality reduction,and expert analysis to extract and visualize dominant research themes.Four key machine learning paradigms are identified:supervised,unsupervised,semi-supervised,and reinforcement learning.Among them,supervised methods,particularly neural networks and support vector machines,are the most frequently applied,showing accuracies above 95%in controlled conditions.The analysis also reveals growing use of semi-supervised and hybrid approaches to overcome data scarcity.Commonly monitored variables include irradiance,voltage,and current,while the most studied faults are shading,open-circuit,and degradation.Several open-access datasets supporting fault diagnosis research are catalogued.Overall,the proposed method enables a more objective and scalable review process and uncovers emerging trends,such as the shift toward lightweight artificial intelligence for edge deployment and frugal diagnostic architectures.The methodology is scalable and adaptable to other domains facing similar challenges in knowledge synthesis and system monitoring.展开更多
基金Artificial and Natural Intelligence Toulouse Institute ANITI funded by the France 2030 program under the Grant agreements n◦ANR-19-PI3A-0004 and n◦ANR-23-IACL-0002the SticAmSud project HAMADI 4.0“Hybrid Algorithms based on Models and Data in Industry 4.0”,n◦22-STIC-06.
文摘Timely fault detection in photovoltaic systems is critical for ensuring energy efficiency,reliability,and cost-effectiveness.However,the nonlinear and weather-dependent behavior of photovoltaic systems poses challenges for accurate diagnosis.This study presents a large-scale review of 983 scientific publications on artificial intelligence-based photovoltaic fault detection,using a novel methodology called Topic-tSNE Fusion.This approach integrates topic modeling,dimensionality reduction,and expert analysis to extract and visualize dominant research themes.Four key machine learning paradigms are identified:supervised,unsupervised,semi-supervised,and reinforcement learning.Among them,supervised methods,particularly neural networks and support vector machines,are the most frequently applied,showing accuracies above 95%in controlled conditions.The analysis also reveals growing use of semi-supervised and hybrid approaches to overcome data scarcity.Commonly monitored variables include irradiance,voltage,and current,while the most studied faults are shading,open-circuit,and degradation.Several open-access datasets supporting fault diagnosis research are catalogued.Overall,the proposed method enables a more objective and scalable review process and uncovers emerging trends,such as the shift toward lightweight artificial intelligence for edge deployment and frugal diagnostic architectures.The methodology is scalable and adaptable to other domains facing similar challenges in knowledge synthesis and system monitoring.
