## 1 Introduction  
During the previous decade, photovoltaic (PV)-based electric generating has been a developing area of research in the industry domains [1; 2], where GCPV systems have experienced the strongest growth [3]. Moreover, the operation of high-efficiency photovoltaic systems has taken a major significance and a top priority, and a big challenge [4]. In fact, many faults can occur and damage this kind of system, these faults can be categorized into three main classes: abrupt, incipient, or intermittent faults [5]. Indeed, line-to-ground or line-to-line, short circuits, connector disconnection, open circuits, hot spots, and junction box failures are kinds of abrupt faults that can occur instantly often as a result of damage to the PV array. Because of their slower dynamics and smaller amplitudes, incipient faultsare generally considered the most difficult faults. They can cause gradual damage to the PV cells, and lead to major problems if not detected early [6]. Therefore, these kinds of faults can occur on both DC and AC sides. PV module defects for instance delamination, yellowing, and browning of solar cells, cracks, gaps, bubbles, and defects in the anti-reflective coating are examples of DC-side incipient faults [7]. Wiring degradation, Insulated Gate Bipolar Transistor (IGBT) faults, islanding, overheating, and aging are all AC-side faults. Environmental stress or partial shading are kinds of intermittent failures that vary over time [5,8]. Thus, it is important to diagnose identify thereby forecast these faults early. Therefore, the demand for FDD algorithms is growing with the speedy growth of information and automation technologies, and data-driven process control approaches are being continuously enhanced. Indeed, different techniques and strategies have been developed in the literature. For instance, the authors in [9], employ and present the initial results of an extensive, Long-Term study of the forecasting of voltage sags in distribution networks. The overriding objective of this research is to give the network operators proper algorithms that will allow them to forecast how many voltage sags will occur and the sites at which they are likely to occur. The authors in [10], employed a Domain Reflectometry (TDR) technique to locate a failed PV module in a PV array, noting that the technique may also be used for fault localization and detection. The research in [11] provided a diagnostic method based on observing the magnitudes of various essential measurable frequency components for a DC-DC boost converter and a voltage source full-bridge inverter. A fault detection strategy for the GCPV systems using a wavelet transform (WT) is proposed in [12]. Using power losses analysis, the authors in [13] proposed a new statistical signal processing method for PV system (PVS) monitoring and fault detection. A strategy for automatic failure detection in (PVS) based on parameter extraction techniques was proposed in [14]. To diagnose faults in (PVS), a statistical technique based on an exponentially weighted moving average chart was developed in [15]. While, in [16], authors presented an approach based on I-V characteristics analysis in order to detect PV array faults. In [1,17,18,19], additional multivariate and univariate statistical techniques for PV fault detection were reported. In [20], an approach based on the estimating PV module's crucial parameters was presented. A new algorithm for detecting faults in PV modules was developed in [21]. The presented method in [22] allows the identification of three major stages of faults, including faults in the string, faults in the module's string, and a group of diverse failures, for example, aging, MPPT errors, and partial shadow. In [23], a fault detection method that compares the current and the previous situations in a defective PV array (PVA), has been developed. The authors in [24] presented a technique for determining the approximate position of faulty PVM in parallel or series PVA.