## 4 Conclusions  
In recent years, many efforts have been devoted to developing various methods for detecting and classifying the Line-to-Line (LL) faults. In some of these methods, little attention has been paid to the presence of fault impedances or the mismatch levels, which are the main challenges in diagnosing this type of faults. However, only a few studies have considered these two important conditions but they could not accurately detect and categorize the LL faults. In this study, a novel intelligent and autonomous method was proposed to detect and classify LL faults precisely. This proposed method has two main stages. In the first stage, the key features were extracted through analyzing the I-V characteristics under various LL fault events and normal operation. In the second stage, a genetic algorithm (GA) was used for parameter optimization of the kernel functions used in the Support Vector Machine (SVM) classifier and feature selection in order to increase the prediction accuracy and diminish the required training dataset to build the SVM model. The main aim of this study was to achieve a small training dataset for the learning process and also obtain a higher accuracy in LL fault detection and classification in the different situations, including low mismatch level and high fault impedance. In this study, the Gaussian kernel function has obtained the highest accuracy with the lowest selected features in both detection and classification processes. In this study, four kernel functions, namely, linear, polynomial, Gaussian, sigmoid, were also applied in the SVM models related to detection and classification of LL faults in order to select the best kernel. The results have demonstrated that the linear and Gaussian kernels have a higher performance compared to the other two kernel functions. However, the Gaussian kernel function has obtained the highest accuracy with the lowest selected features in both detection and classification processes. This kernel function has obtained an average accuracy of 96% and 97.5% in the learning process of detection and classification models, respectively. It should be noted that the number of the selected features is equal to three and two for detection and classification models, respectively. This proves the high capability of Gaussian kernels in the diagnosis of LL faults with the lowest necessary features. So, the proposed method was able to detect and classify various types of LL faults with an accuracy of around 97%.  
The proposed method has provided various novel approaches for the autonomous monitoring of PV systems. In future work, we plan to validate the proposed method by experimental measurement.  
Conceptualization, invention, investigation, simulation, visualization and writing--A.E.; Conceptualization, invention, validation and supervision--J.M.; Conceptualization, invention, investigation, validation, visualization, writing, editing and supervision--M.A.; Reviewing, writing and editing--A.H.M.E.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We like to acknowledge the COST Action CA16235 PEARL PV, Working Group 2 (WG2) for supporting this research.  
The authors declare no conflict of interest.