# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 5 The Latest Research on Anomaly Detection (a.k.a. Fault Detection) and Diagnosis in PV
#### 5.0.1 Detectable Faults by ML-Based Methods  
Faults in PV can be of different types; for in-depth analysis of faults that can adversely affect PV plants see [61; 62]. In literature, the vast majority of works deal with four types of faults: short circuit (SC), open circuit (OC), partial shading (PS) and abnormal ageing. For these types of faults, the most employed solution is based on an MLP ANN that considers as inputs current or voltage related to string/array/panel, so the most frequent variables taken into account are voltage at MPP (V\({}_{\text{MPP}}\)), current at MPP (I\({}_{\text{MPP}}\)), OC voltage (V\({}_{\text{OC}}\)) and SC current (I\({}_{\text{SC}}\)), almost always supported by environmental variables such as ambient and module temperature and solar irradiance at the panel level. These models necessarily require a labelled dataset and are mainly based on the difference between the models'predicted system performance and the real measured one. Many ML-based models that employ SNN apply input pre-processing as Discrete Wavelet Transform (DWT); this is a typical form of feature engineering that has proven to be beneficial to improve the FD accuracy of the model. For the faults described so far, the models usually employed consist of SNNs of various typologies, but also DT ensembles such as RF or 1-SVM. Considering faults detectable using image analysis as module delamination/crack, hotspot or soiling (dust and birds' droppings), this is a field dominated by DL and especially CNNs trained on thermal infrared (IR) images acquired by UAV. For detecting faulty cells or modules electroluminescence (EL) images are also considered, while at the array level only IR images, generally EL images, embed more fault information and are the preferred type of images. The type of CNN used in this field varies from pre-trained known CNN architectures such as LeNet and VGG-16 to custom architecture. This is a field where Transfer Learning [38] can be very beneficial and where data augmentation techniques are also very common (image rotation, flip, etc.). Although CNNs are particularly suited to dealing with 2d data, i.e., images (usually IR or EL), some interesting results have been obtained by treating a 1D signal, such as a current-voltage (I-V) curve, as a 2D feature using, for example, a scalogram and combining a CNN with an LSTM. In Table 4 are reported some recent, always in the range 2018-2021, review papers dealing with ML-based models to detect faults/anomalies in PV. The remainder of this paragraph is devoted to the latest research paper dealing with ML methods for anomaly/fault detection in PV. Paper [68] focuses its attention on the detection of hotspots using a hybrid based SVM model trained using infrared thermography (IRT) images; it classifies panels into three categories: healthy, non-faulty hotspot and faulty hotspot. The novelty of this paper resides in the pre-processing phase of the IRT images acquired by handheld a FLIR camera horizontally aligned to PV panels of a PV system made up of 22 modules. The image feature extraction pipeline here proposed results in 41 features: