## 1 Introduction  
With the increasing global growth of photovoltaic (PV) installations, autonomous monitoring of PV systems has become increasingly important to diagnose system and component failures as fast as possible in order to ensure the long-term reliability and service life of PV systems [1; 2; 3]. PV arrays may fail due to internal and the external causes. The line-to-line (LL) fault is one of the major catastrophic failures that can lead to lower system efficiency and even worse, to fire disaster. An LLfault is defined as an unintentional connection between two points in a PV array with a different potentials [4; 5]. LL faults might occur in PV arrays due to mechanical damage, water ingress, DC junction box corrosion, and hot spots caused by the back-sheet failures. For a better understanding of LL faults, Figure 1 shows the most common configuration of a PV array, which includes the PV arrays affected by two types of LL faults, and the conventional protection devices such as Over Current Protection Devices (OCDD), namely fuses. Here, the intensity of an LL fault is expressed by the level of the mismatch, which is specified by the number of modules affected by LL faults. As shown in Figure 1, F1 represents a LL fault with 10% mismatch due to the short-circuit that occurs between the external contacts, i.e., the incoming and outgoing connection of a single PV module in a string. Similarly, F2 indicates a LL fault with 20% mismatch and resistance connection. Generally, LL faults cause a sudden drop in voltage in the faulty string, which results in a back-feed current from the healthy strings to the faulty ones. Therefore, fuses are used to interrupt the faulty PV string from the currents of the fault. According to the National Electrical Code, the fuse rating should be greater than 2.1 I\({}_{\text{SC}}\) (short-circuit current at standard test condition (STC)) in PV strings. Nevertheless, fuses may fail to detect LL faults under low mismatch level or high fault impedance. Because the faults' current caused by these situations may be inadequate to melt the fuse, hence, faults may remain undetected in the PV arrays [6]. Blocking diodes are installed in each string to prevent the back-feed current. However, the protection devices may fail to interrupt the fault current even under STC due to the presence of a blocking diode. Moreover, these diodes may fail and cause unnecessary losses. Numerous methods have been presented for the diagnosis of LL faults in the literature [7; 8; 9]. According to the approach that each method has adopted to identify faults, these methods can be divided into three categories. The first category is a comparison between real and predicted parameters. For instance, in [10], a one-diode model was combined with an Exponentially Weighted Moving Average (EWMA) chart to investigate for any deviations from healthy conditions in PV systems. The difference between the measured and the predicted parameters is captured as residuals that are used as fault indicators. [PERSON] et al. [11] presented a method based on the T-test statistical analysis, which used Voltage Ratio (VR) and Performance Ratio (PR) to specify the fault condition and the distinct faults, whereas the authors in [12] have used fuzzy-based decision making instead of the T-test. Moreover, there are  
Figure 1: Modeling and schematic of photovoltaic (PV) arrays in MATLAB/Simulink. a few methods that compare real-time parameters with their threshold limits. A threshold-based method by analysis of perturbation and observation (P&O) MPPT has been proposed in [13]. A fault diagnostic technique has been proposed in [14] based on the characteristics and magnitudes of the voltage waveform of each string in a PV array. This method requires two external sensors to collect data because two voltage transducers have been used in each string.