## 1 Introduction
Comparison between a baseline curve (no fault) and cases with partial soiling and cell crack system modes are presented [22]. [PERSON] et al. proposed a hybrid algorithm by combining the symmetrized dot pattern (SDP) with a convolutional neural network (CNN) for common faults such as poor welding, breakage and bypass diode failure. Comparison with a fault-free module was conducted and this study successfully combined SDP with CNN to develop a PV module fault, with recognition accuracy reported to 99.88%. On the other hand, methods to be applied in a grid-connected operation were also presented. [PERSON] et al. developed a genetic-algorithm (GA)-based ANN. The proposed method is applied in GCPV systems under normal and faulty conditions [23]. [PERSON] et al. created a detection technique based on the comparison of calculated and measured DC power, with a predefined threshold value. The method uses a simulation tool for calculated values and generates a diagnostic signal to show normal or abnormal operation in a 2 kWp GTPV plant [24]. [PERSON] et al. propose a simple technique for the detection and classification of faults occurring in PV systems based on a fuzzy logic controller. The method introduced a 'fault index', which measures the degree of deviation from the normal operating conditions and is applied in a 5 kWp grid-connected PV system [25]. Another aspect of fault detection aims in special types of faults as faulty inverters' operation and problems with arc discharge at the DC side. [PERSON] et al. proposed a fault detection method for DC series arcs based on the combination of adaptive local mean decomposition (ALMD), multiscale fuzzy entropy (MFE), and support vector machine (SVM) algorithms for PV systems [26]. [PERSON] et al. propose a model for the detection of inverters' faults, especially those observed at the inverter's power MOSFETs. The method is based on periodically monitoring the variation in the harmonics of the inverter's currents [27]. Improved fault detection algorithms are also comparatively tested by [PERSON] et al. [28] in small pilot PV installations by the use of non-iterative FF ANN and radial basis function (RBF) ANN. Further development in the topology and training algorithms include non-iterative neural-like structures based on the successive geometric transformations model (SGTM), which has been successfully applied to big data in regression and classification tasks in related sectors [29]. Finally, IR thermography is a method that gives motivation to a fair number of researchers. [PERSON] et al. proposed an IR thermography inspection strategy for PV plants based on a two stage aerial inspection and carried out on the ground. The method was applied to a 100 kW PV plant. The types of faults addressed were broken glass in a PV module, partial shading, problems involving PV module technology and open-circuit PV modules, along with other incidents [30]. [PERSON] et al. proposed an algorithm in order to analyze the infrared images of solar panels. The proposed method uses a convolutional neural network with SVM (CNN-SVM) to classify enhanced images in order to conduct non-physical visual recognition and fault detection analysis [31]. The current work focuses to the application of ANN in the fault detection of grid-connected PV plants. The main contribution is the analysis and evaluation of already known fault types on grid- connected systems with the aid of ANN, aiming at formulating a procedure to enable fault diagnosis through the quantification of statistical metrics characteristic to each type of fault. To this end, the evaluation of the same type of ANN with different inputs is carried out. The novelty of the proposed methodology is based on the correlation of already known faults in grid-connected PV systems, with the statistical deviation of their performance metrics when they are observed with the aid of an ANN. The organization of the remaining part of this paper is as follows: Section 2 presents the testing setup and the fault detection methodology.