## 1 Introduction
The article proposes the use of a kNN model to detect and classify faults in a PV system, including bypass diode, line-to-line, and open-circuit faults. The results from both simulated and experimental data show an accuracy of 98.7% [3]. [PERSON] et al. demonstrated the effectiveness of the SVM model in identifying various faults, such as line-to-line faults, open circuit, partial shading, and MPPT failure. The algorithm used current and voltage data from the PV  
Figure 1: Annual evolution of the global world capacity of PV installations (TrendForce, 2023). field as inputs, resulting in a fault classification accuracy of 99.4% for detection and 98% for diagnosis [29]. [PERSON] et al. developed a Mamdani fuzzy logic controller to detect bypass diode faults and hot spots using voltage drop, percentage of open circuit voltage, and short circuit current as input data. The obtained results demonstrate that the proposed method can accurately identify 13 types of defects, including the hot spot defect, with 96.7% accuracy [30; 31; 32]. Additionally, a C4.5 decision tree-based algorithm is proposed to diagnose and detect string, short circuit, open circuit, and line-to-line faults in a grid-connected PV system. 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. 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. However, the implementation rate of ensemble algorithms remains very low. This article proposes a new classifier Extra Trees (ETC) and its algorithm for the rapid diagnosis of faults in photovoltaic systems. As demonstrated in various publications across different fields, including economics [38], medicine [39; 40], hydraulic engineering, and telecommunications [41; 42; 43], the Extra Trees algorithm has shown robustness to noise, a significant reduction in bias errors, and lower variance compared to other models such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Decision Trees (DT) [39]. Furthermore, this algorithm exhibits a lower computational complexity rate compared to other Machine Learning (ML) classification models, such as DT, Adaptive Boosting (AdaBoost), Naive Bayes (NB), SVM, RF and KNN [39]. To the best of our knowledge, previous research on fault detection and classification in PV systems has not yet considered the Extra Trees Ensemble (ETC) algorithm. Therefore, in Section 5, we provide a detailed presentation of the Extra Trees model. 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. The purpose of this paper is to provide technicians, engineers, and researchers with information to guide them in identifying the primary faults to check in the event of a PV system failure.