# 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
\begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline
**Year** & **Reference** & **Notes** \\ \hline
2021 & [63] & A review of AI-based methods for remote sensing and fault detection and diagnosis (FDD) in PV emphasizing the applicability of models and the use of IoT technologies for remote monitoring and diagnosis. \\ \hline
2020 & [64] & A very comprehensive review on fault detection in PV using both SNNs and DL. Analysis related to the years 2009–2020. MLP and CNNs result as the more diffused methods employed in this field. Some public datasets (cell images) were reported. Proposes the build of a large open database of healthy and faults modules/plants (1D and 2D images) \\ \hline
2019 & [65] & Four major faults are analyzed: ground, line-line, arc and hot-spot. For each fault are proposed both conventional and advanced methods to deal with them: ML-based (MLBTs), reflectometry-based, statistical and signal based and comparison based. Proposes a scoring system to ranks methods. \\ \hline
2018 & [66] & A review of applicable methods, ML-based but also statistical-based, to FDD in PV. Highlights that most methods employ I-V curve data but also irradiance and module temperature. \\ \hline
2018 & [62] & An in-depth analysis of all major faults that can affect PV systems is accompanied by a complete list of methodologies that can be employed to detect and diagnose faults. Only a small section is devoted to ML-based methods. \\ \hline
2018 & [61] & After describing all major faults that can occur in PV, it focuses on FDD methods especially suited for faults occurring in a PV array: statistical, I–V analysis, power loss analysis, voltage and current measurement and AI-based. This paper concludes by highlighting the pro and cons of each method with some recommendations and insight into possible future trends. \\ \hline
2018 & [67] & Analyzes all major faults that can affect PV with a review of methods in the literature for PV fault monitoring and detection. Emphasizes how statistical methods do not require previous data but cannot identify failure types. On the other hand, numerical methods can detect failure types, but require knowledge of previous data. Knowledge model-based methods using residual current voltage or power can provide fault detection and identification but require historical data and also meteorological ones. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Review papers for fault/anomaly detection and diagnosis in PV. Publication year considered: 2018–2021. 3 RGB, 12 contrast, 12 correlation, 3 energy, 1 Histogram of Oriented Gradient and 10 Local Binary Pattern. The feature extraction proposed results in an improvement in terms of accuracy results for the following classification algorithms: KNN, n-Bayes, Quadratic Discriminant Analysis (QDA) and bagging ensemble (BE). The SVM performed the best also in terms of computing time (k-fold CV methodology applied to derive all metrics). An LSTM NN is used in [69], combined with DWT as a feature extraction phase, to detect High Impedance Fault (HIF) and four other faults coming from an IEEE 13-bus system with a solar PV network simulated in MATLAB/Simulink. Results from the proposed LSTM as classifier are compared with other ML-based methods: SVM, Naive Bayes, J48 Decision Tree. Models performance, defined utilizing several metrics (F-Measure, Recall, Precision, CM, Kapps Statistics) clearly show the LSTM model as the best performing. Line-to-Line (LL) faults are automatically detected in [70] using an SVM model whose hyper-parameters are selected using GA. This model employs features extracted from DC I-V data resulting from a simulation model (developed with Matlab/Simulink) of a PV plant. GA is also used to extract optimal features for detect LL faults even in case of low mismatch and high impedance. A total of ten features are extracted from the simulated data, and all features are related to I-V curves under normal and fault events based on three points: short circuit current, MPP and open-circuit voltage.