## 1 Introduction
For detecting DC cable faults and PV series arc failures, the authors proposed in [25] a novel differential current-based quick detection and accurate failure localize estimate method. On a GCPV system, FDD has been employed using a reduced Kernel Random Forest (KRF) based on K-means clustering and a Euclidean distance-based KRF [26]. Additionally, in order to optimize the voltage profile of distribution systems, centralized control is adopted and implemented in [27] for determining the set points of the controllers of the distributed energy resources connected to the grid. The study in [28] illustrated the use of an artificial NN (ANN) technique to diagnose GCPV system faults. A fault detection strategy for PV modules under partially shaded statues is proposed in [29], which utilizes an artificial NN to predict electrical outputs and detect possible anomalies in the PV module using real time correlation of estimated and measured performances under variable conditions. The authors in [30] used the ANN in conjunction with the traditional analytical approach to provide string-based PV systems with innovative and automatic fault detection and diagnostics. In [31], a bi-directional input parameter integration-based ANN-based PV failure detection technique is developed. A Radial basis Network-based PV array defect detection method is provided in [32]. The authors in [33] employed a novel diagnostic strategy for PV systems based on artificial NNs to identify and classify the diverse failures occurring in the PV array. The work in [34] presents a novel intelligent algorithm for PV system diagnosis and fault detection (IFD). In this work, the ANN algorithm can identify and thereby detect three recurrent states between healthy, string disconnection, and short circuit faults in the PV array. The paper [35] proposes a customized NN algorithm that classifies, and identifies eight diverse commonly occurring PV faults scenarios. The authors in [36] introduce the Laterally Primed Adaptive Resonance Theory (LAPART) artificial NN for PV system fault diagnostics and detection purposes. In [37], the authors used back-propagation ANN, generalized regression ANN, probabilistic ANN, and two radial basis function ANNs (RBF) to detect and locate the most encountered failures in PV installations: short circuit, and open circuit string cases in PV generator. The current work proposes an intelligent fault detection/diagnosis strategy based on Multiscale Principal Component Analysis (MSPCA) and NN classifiers in order to enhance the efficiency of conventional data-driven strategies for monitoring multivariate dynamic systems. Different from the classical and standard diagnosis approaches, the proposed MSPCA-based NN approaches are used to detect and thereby isolate faults. Therefore, the contributions of this work involve three major steps: First, the data are pre-processed by the use of a multiscale scheme in order to remove noise and stochastic observations. Second, the new dataset is fed as input to a PCA method in order to extract and select the most-significant attributes from the GCPV systems in order to improve and accelerate, thereby enhance, model convergence and classification performance and accuracy. After that, the extracted features are fed as inputs to the NN classifiers in order to detect, classify, and distinguish between the different conditions. Additionally, this study is being investigated and established to address and treat all the frequent and potential faults that might occur, damage, and affect PV systems. A total of 21 fault scenarios: line-to-line, line-to-ground, connectivity, and faults that can affect the bay-pass diodes' normal operation are introduced at various levels and locations; each scenario contains a variety of conditions, including simple faults in the \(PV_{1}\) array, simple faults in the \(PV_{2}\) array, multiple faults in the \(PV_{1}\) array, multiple faults in the \(PV_{2}\) array, and mixed faults.