## 1 Introduction  
In recent years, renewable energy sources have gained popularity, with photovoltaic solar energy ranking as the third most developed technology behind hydroelectricity and wind power. According to the \"TrendForce Feb2023\" report, photovoltaic solar energy is experiencing remarkable growth, with an estimated world installed capacity of 350.6 GW by 2023 [1]. The annual evolution of the global installed capacity of PV systems is shown in Figure 1. This growth can be attributed to various factors, including reduced production costs, government support policies, reliability, and the desire for localized energy production. However, despite these benefits, photovoltaic installations may face challenges related to aging and environmental constraints that can impact their efficiency and long-term safety. Exposure to difficult environmental conditions can lead to malfunctions and anomalies that result in power losses or even the risk of fire, depending on the severity of the issue [2]. When the surface of a solar panel system is covered with dust for two months, its performance can be reduced by 8.4% compared to a clean system according to studies [3, 4]. Therefore, it is crucial to be aware of any faults, control them to minimize their occurrence, recover the maximum amount of energy produced, and reduce maintenance costs for the PV system. Several research studies have been conducted to identify the categories of faults and diagnostic techniques for detecting various faults in photovoltaic (PV) systems. Some of these techniques use climate data-independent methods based on the circuit resistance, inductance, capacitance (RLC) and a signal generator to predict faults in PV systems, while others rely on electrical parameters based on current and voltage indicators [3; 5; 6]. It is noteworthy that these methods are not affected by climate data. In recent years, there has been a renewed interest in the industrial applications of digital methods, such as the use of machine learning for vehicle autonomy on public roads and fault diagnosis using data [7]. In the field of photovoltaics, various machine learning models, such as artificial neural networks (ANN), k nearest neighbors (kNN), the Adaptive Neuro-Fuzzy Inference System (ANFIS), Naive Bayes (NB), decision trees (RF), and fuzzy logic, have been successfully employed for fault diagnosis [3; 8; 9; 10; 11; 12; 13]. Several articles have demonstrated the effectiveness of supervised learning algorithms in improving the diagnosis of PV systems with the application of artificial intelligence [14; 15]. Compared to traditional techniques that require more computing time and human expertise, Machine Learning (ML) and Deep Learning (DL) supervised learning algorithms are faster and more efficient in providing diagnostic solutions [14; 16; 17; 18]. For example, [PERSON] et al. proposed a Deep Learning algorithm that combines convolutional and bidirectional recurrent neural networks to detect faults in a PV system [19]. Additionally, several authors have conducted reviews to highlight the effectiveness of Machine Learning and Deep Learning algorithms in diagnosing PV systems, as they accelerate and improve diagnostic solutions for PV systems [20; 21; 22; 23; 24; 25; 26; 27]. This article specifically focuses on supervised machine learning algorithms. To make this happen, the authors propose an ANN model to detect short-circuit faults in a grid-connected PV module. They use [PERSON]'s algorithm Marquardt and the ANN in MATLAB/Simulink. The authors conclude that the algorithm effectively recognizes short-circuit faults through the trained data [11]. Similarly, [PERSON] et al. propose the random forests algorithm to detect partial shading, short circuit, open circuit, and aging faults on a simulated PV field. The study utilized irradiation, temperature, current, and voltage at maximum power point (MPPT) as inputs for the model. A comparison of the results demonstrated the superiority of the Random Forest (RF) model over the kNN and SVM models [28].