## 1 Introduction
In summary, the most important drawbacks in these methods are inaccurate estimation of the PV system model under different conditions and the dependence of these methods on the quality of the threshold limits. The second category of LL fault detection is based on analysis of the output signals. In [15], a quickest fault detection method was developed based on Autoregressive (AR) to detect variations in the output signals and the Generalized Local Likelihood Ratio (GLLR) test to check the faults. [PERSON] et al. [16] proposed a method based on wavelet packets. In this method, the array voltage change, the array voltage energy, and energy of the change in impedance were extracted as features by Discrete Wavelet Transform (DWT) and the threshold limits were used for detecting the faults. In order to extract features from the signals, these methods require extra hardware and software platforms. Therefore, they are very costly and difficult to implement. Machine Learning (ML) is the third category, which is more accurate to detect and classify the faulty conditions in PV systems. For instance, a probabilistic neural network scheme was developed based on information from the manufacturer's datasheet [17]. [PERSON] et al. [18] studied an enhanced machine learning-based approach for fault diagnosis of PV systems by extraction of PV modules' parameters. In [19], random forest learning is utilized as a kind of ensemble learning method. Two models have been developed by the C4.5 decision tree algorithm for fault detection and diagnosis in [20]. [PERSON] et al. [21] developed a fault detection model for a PV system based on conventional neural networks and electrical time series graphs. In general, little attention has been paid to LL faults under low mismatch or high-impedance in the aforementioned methods, which are the main challenges for detection and classification of LL faults in PV arrays. The studies [22; 23; 24] have tried to present multiple algorithms as an attempt to detect PV faults under these conditions. However, these methods also have two major drawbacks. First, these methods require a big dataset for the learning process, and another drawback of these methods is related to low accuracy in detecting the LL faults with high impedance or a low mismatch level. To address the challenges discussed above, here, we propose two novel concepts to detect and classify the LL faults efficiently using a Support Vector Machine (SVM) classifier. The first concept has been developed via a Simulink-based model of PV arrays to extract the main features by analyzing characteristics of the Current-Voltage (I-V) curve under normal and LL fault conditions. In the second concept, a Genetic Algorithm (GA) has been applied to optimize the feature subset selection and SVM parameters in order to obtain a higher accuracy for classifying the LL faults. This method not only reduces the number of datasets required for the training of the SVM classifier, but also it improves the accuracy of LL fault detection and classification. The main contributions of this study are summarized as follows:  
1. The main features of the faults have been extracted from I-V characteristics of PV arrays using a Simulink-based model for distinguishing between faulty and normal conditions. 2. To the best of our knowledge, for the first time, a Genetic Algorithm (GA) has been developed for feature selection and parameters of classifier optimization in fault detection and classification for PV systems. This algorithm has been implemented using Python. 3. The proposed method is able to detect and classify LL under a wide range of critical situations, even under low-mismatch levels and high-impedances. The methodology and the details of the proposed method process are explained in Section 2. Section 3 presents the results. Section 4 summarizes the main findings of our study, with recommendations for further research.