## 3 Machine Learning and Fault Diagnosis
### Hybrid Models for Diagnosing PV Systems  
At this stage, most of the models used provide accurate results in fault diagnosis, each with its own strengths and weaknesses. However, these models, although effective, do not realize the full potential of ML on all data samples. One way to improve the performance of ML in task execution is to combine the advantages of simple algorithms in order to solve problems that they are incapable of solving alone. As a result, some authors have proposed improved and/or hybrid versions of simple algorithms to carry out diagnostic work in photovoltaic systems. The ANFIS model (Adaptive Neuro Fuzzy Inference System) is an adaptive neuro fuzzy inference system that combines the advantages of both fuzzy logic and artificial neural networks. The detection and classification of system faults is proposed by deploying two subsets of the ANFIS model, such as the ANFIS partition network (ANFIS GP) and ANFIS subtractive clustering (ANFIS SC). The results of the statistical analysis based on RMSE (Root Means Square Error) show that ANFIS SC is better than ANFIS GP [108]. [PERSON] et al. proposed the ANFIS model for modeling and the identification of the PV system to detect short-circuit and open-circuit faults in the single-phase photovoltaic inverter. The model detected the simulated defects with an accuracy rate of 100% with a fast execution time [109]. A review based on the applications of the fuzzy neuro network algorithm and its variants is proposed in order to show the effectiveness of the algorithm in the construction of systems [110]. Likewise, a new detection method based on the combination of the three algorithms SVM, KNN, and NB made it possible to extract the electrical characteristics in order to analyze and detect the line-line fault on the basis of the current-voltage curve of the PV system [97]. A grid-connected PV fault detection and diagnosis technique based on the combination of three algorithms KNN, DT and SVM showed a better performance with an accuracy rate of 99.96% [74].  
To improve the performance of a PV system, a hybrid algorithm based on principal component analysis and support vector machine is proposed to detect bypass diode and series resistance faults. The results obtained after the detection of all types of defects show an accuracy of 99.96% and 99.93%, respectively [96; 98]. Also, a fuzzy k-nearest neighbors (FKNN) model was used to detect short-circuit, open circuit and irradiation faults in a spacecraft powered by a solar system. The results obtained are significantly better compared to the KNN model, i.e., an accuracy of 99.4% compared to 91.8% for KNN [99]. In the literature, several reviews on the diagnostic methods for PV systems have been carried out in recent years [7; 111; 112]. In these reviews, the presence of hybrid models such as ANFIS, FKNN and so on is remarkable, as shown in Table 2. Another type of hybridization involves combining weak classifiers to obtain a strong classifier for better prediction. This algorithm is based on the principles of Bagging and Boosting, using decision trees as a basic algorithm. It proceeds by voting or averaging the individual performances of each weak classifier to obtain an optimal performance. Therefore, it is called an ensemble learning algorithm.