Photovoltaic systems are prone to breaking down due to harsh conditions. To improve the reliability of these systems, diagnostic methods using Machine Learning (ML) have been developed. However, many publications only focus on specific AI models without disclosing the type of learning used. In this article, we propose a supervised learning algorithm that can detect and classify PV system defects. We delve into the world of supervised learning-based machine learning and its application in detecting and classifying defects in photovoltaic (PV) systems. We explore the various types of faults that can occur in a PV system and provide a concise overview of the most commonly used machine learning and supervised learning techniques in diagnosing such systems. Additionally, we introduce a novel classifier known as Extra Trees or Extremely Randomized Trees as a speedy diagnostic approach for PV systems. Although this algorithm has not yet been explored in the realm of fault detection and classification for photovoltaic installations, it is highly recommended due to its remarkable precision, minimal variance, and efficient processing. The purpose of this article is to assist technicians, engineers, and researchers in identifying typical faults that are responsible for PV system failures, as well as creating effective control and supervision techniques that can minimize breakdowns and ensure the longevity of installed systems.

The application of machine learning techniques for monitoring and diagnosing faults in photovoltaic (PV) systems has been shown to enhance the reliability of PV power generation. This research introduced a novel machine learning classifier for fault diagnosis in PV systems, utilizing an ensemble algorithm known as extra trees (ETC). The study initially proposed a system with two PV modules and developed a low-cost Arduino-based data logger to gather data from the PV system in free-fault and faulty conditions. Subsequently, the study evaluated six other advanced classifiers for fault diagnosis in PV systems, namely logistic regression (LR), k-nearest neighbor (kNN), support vector machine (SVM), decision tree (DT), AdaBoost, and random forest (RF) models using the collected data from the proposed PV system. The assessment of the various models' performance indicated that the extra trees model exhibits superior classification capabilities for partial shading (PS), open circuit (OCF), partial shading with bypass diode disconnected (PSBD), and combined partial shading with bypass diode disconnected plus open circuit (PSBDOC) faults. The results demonstrated that the new ETC classifier achieves an accuracy of 92%, surpassing the 91%, 87%, 7%, and 59% accuracy of the RF, DT, kNN, and LR classifiers, respectively. This highlights the effectiveness of the extra trees model in enhancing fault detection and classification by distinguishing between open circuits and twin faults. Consequently, these results can be utilized to develop advanced diagnostic tools for photovoltaic systems, thereby improving the reliability of solar technology and accelerating the rate of installation.

The rapid expansion of the solar industry has underscored the importance of photovoltaic installations in the ongoing transition to sustainable energy. With this growth comes the crucial task of effectively monitoring and controlling the power generated. Photovoltaic systems are particularly vulnerable to defects due to their exposure to challenging environmental conditions, which can lead to reduced power output and an increased risk of fire. Therefore, a thorough analysis of any faults is essential in order to mitigate potential damage to the system. The present study proposes a comprehensive analysis of the behavior of a photovoltaic generator comprising four modules. MATLAB/Simulink software is used to model the generator in healthy operation. Subsequently, a simulation of the generator in faulty conditions is conducted, considering four fault cases: partial shading (PS), open circuit fault (OCF), bypass diode disconnected (PSBD), and twinned fault bypass diode disconnected plus open circuit (PSBDOC). A detailed examination of the simulation results for the faults above reveals that the twinned fault results in a substantial reduction in the output current, as well as an elimination of the open circuit voltage of the photovoltaic generator. This contrasts the behavior observed in a system comprising two modules, wherein the open circuit voltage remains unaltered. This particular fault offers a compelling rationale for the monitoring of photovoltaic installations, to enhance overall productivity while avoiding any potential damage to the system.

In recent years, fault diagnosis has become a major concern for ensuring the sustainability of photovoltaic systems. This article aims to develop a low-cost data acquisition device based on the Arduino card to collect data using sensors on two photovoltaic modules. Four defects were intentionally created on a photovoltaic panel and analyzed individually based on the evolution of current and voltage parameters over time. The results indicate that in the event of a fault, the current drops suddenly with a slight decrease in voltage. Under normal conditions, the maximum observed current was 3.8 A for an irradiation of 904 W/m2, compared to 2.2 A for 909 W/m2 and 32.78°C in the event of partial shading. This value further decreases to a maximum of 1.8 A for 867 W/m2 in an open circuit. The observation reveals that the partial shading fault with a disconnected bypass diode and open circuit has similar characteristics to the open circuit fault. However, it is important to note that this cannot be generalized as the fault only occurs in a configuration of two PV modules in parallel.