## 1 Introduction
The processing of the difference between the measurements and model predictions, used as fault indicators, was carried out through the application of an exponentially weighted moving average (EWMA) monitoring chart [6]. Another statistical approach is based on labeling faults at over 100 PV sites in the United States, by the use of machine learning (ML) algorithms and hidden Markov modeling (HMM), which allow the labeling of historical behaviors without a manual setting of threshold [11]. [PERSON] et al. proposed a model for the detection of faulty modules in a string, faulty string, faulty inverter and false alarm, a group of faults which include partial shading, the ageing of PVs and inverter MPPT errors. The idea behind this method is the comparison of absolute error on the performance ratio (PR) with a threshold, that enables generating a diagnostic signal [12]. Research on the health monitoring of PV installations is profiting from the quite significant, parallel research efforts applied to the vibration-based health monitoring of civil structures, affected by environmental factors (traffic, ambient temperature, noise), which change their structural dynamic characteristics, covering up those coming from structural damage. For example, [PERSON] et al. employed the support vector machine (SVM) and moth-flame optimization (MFO) [13], as well as an autoregressive (AR) time series model with two-step artificial neural networks (ANNs) [14] to identify damage under temperature variations. [PERSON] et al. compared the behavior of a multiple linear regression, ARMA model, CNN-simple, and CNN-LSTM and observed that the CNN-simple and the CNN-LSTM methods perform best for all 1-h, 1-day and 1-week predictions, with the CNN-LSTM providing better results on certain occasions [15]. Many researchers face the degradation problem with methods applied in specific experimental setups in off-grid operation. [PERSON] et al. proposed the use of simple machine learning tools to predict the global PR based on the following variables: global irradiance in-plane of array, and ambient temperature. Two regression models were applied: a multiple linear regression (MLR), and random forest (RF) algorithm, which is simpler than ANN [16]. [PERSON] et al. propose a machine learning method for fault diagnosis and classification, which is based on the principal component analysis (PCA) technique. The proposed approach is applied in a PV system comprising three 4 kWp PV arrays, each driven by individual MPPT trackers [17]. [PERSON] et al. proposed a method based on a fuzzy logic filter (FLF), which relies on PCA and moving window principal component analysis (MWPCA). The method was applied in three inverters, with polycrystalline, monocrystalline and thin film PV panels connected, respectively, in Malaya University. The results demonstrate the method's effectiveness in detecting different types of faults with high accuracy [18]. [PERSON] et al. proposed a PV-fault identification method based on improved deep residual shrinkage networks (DRSN). The method was able to identify short-circuit faults, partial-shading, abnormal aging and hybrid faults in a 6.48 kWp experimental PV field [19]. [PERSON] et al. proposed a multi-output ANN for fault detection on the DC side of a photovoltaic system. A comparison of \(\mathrm{I_{mpp}}\) and \(\mathrm{V_{mpp}}\) values, obtained from the models based on data from the manufacturer's data sheets, proved the effectiveness of the proposed method [20]. [PERSON] et al. propose an ANN for faults and degradation phenomena occurring in PV panels. The method has two stages: one for predicting the single diode model parameters under normal operation, and one for the degraded condition. Comparison between the two stages is used for the identification of the degradation type. The method was tested using experimental data of I-V curves from the NREL database [21]. [PERSON] et al. propose an approach that utilizes physics-based simulations of string-level IV curves.