The renewable energy industry is growing faster than ever before and in particular solar systems have significantly expanded. Abnormal conditions lead to a reduction in the maximum available power from solar (photovoltaic) systems. Thus, it is necessary to identification, detection, and monitoring of various Faults in the PV system that they are the key factors to increase the efficiency, reliability, and lifetime of these systems. Up to now, Faults on PV components and systems have been identified; some of them have physical damage on PV systems and some of them are electrical Faults that occur on the DC side or AC side of the PV system. Here, the Faults will be divided into groups based on their location of occurrence. This paper provides a comprehensive review of almost all PV system Faults and Fault detection techniques of PV system proposed in recent literature.

Fault classification in distance protection of transmission lines, with considering the wide variation in the Fault operating conditions, has been very challenging task. This paper presents a Probabilistic Neural Network (PNN) and new feature selection technique for Fault classification in transmission lines. Initially, wavelet transform is used for feature extraction from half cycle of post-Fault three phase currents at one end of line. In the proposed method three classifiers corresponding with three phases are used which fed by normalized particular features as Wavelet Energy Ratio (WER) and Ground Index (GI).The PNNs are trained to provide Faulted phase selection in different ten Fault types. Finally, logic outputs of classifiers and GI identify the Fault type. The feasibility of the proposed algorithm is tested on transmission line using PSCAD/EMTDC software. Variation of operating conditions in train cases is limited, but it is wide for test cases. Also, quantity of the test data sets is larger than the train data sets. The results indicate that the proposed technique is high speed, accurate and robust for a wide variation in operating conditions and noisy environments.

Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and safety remain critical because they are prone to various Faults, mainly when operating in harsh environmental conditions. This study addresses these issues by exploring Fault detection and classification in PV arrays using neural network (NN) -based techniques. A PV array model, consisting of 3x6 PV modules, was simulated using MATLAB Simulink to replicate real-world conditions and analyse various Fault scenarios. An open circuit, a short circuit, and a degrading Fault are the three types of Faults considered in this study. The NN was trained on a dataset generated from the MATLAB Simulink model, encompassing normal operating and Fault conditions. This training enables the network to learn the distinctive patterns associated with each Fault type, enhancing its detection accuracy and classification capabilities. Simulation results demonstrate that the NN-based approach effectively identifies and classifies the three types of Faults.

Symmetrical nature of mean of electrical signals during normal operating conditions is used in the Fault detection task for dependable, robust, and simple Fault detector implementation is presented in this work. Every fourth cycle of the instantaneous current signal, the mean is computed and carried into the next cycle to discover nonlinearities in the signal. A Fault detection task is completed using a comparison of two sub cycle means, and the same concept is extended to Faulty phase classification. Under various Fault and system operating situations, the suggested technique is assessed for regular Faults, remote end Faults, high resistive Faults, and high impedance arcing Faults. This paper's extensive case studies illustrate the suggested scheme's simplicity, computational flexibility, speed, and reliability. The suggested approach yields 100% consistent results in 4-8 msec detection time.

Considering the sensitivity of high voltage direct current transmission system protection and the difficulty in identifying external DC Faults, this paper presents two methods for Fault detection and classification in VSC-HVDC transmission lines. These studied methods are evaluated in terms of efficiency and accuracy. This research is focused on DC Faults at different distances along the lines. DC line current and voltage are selected as input to wavelet transform, and in the next step, unique and valuable features of each signal are extracted with modern signal processing methods, and then these features are used as input data for algorithms to detection and classification Faults. Deep Neural Network (DNN)  and Support Vector Machine (SVM) have been investigated to detect and classify Faults. In the next step, the efficiency of these algorithms was investigated and analyzed in noise conditions. The innovation of this research is replacing a new method of extracting features from the fractal dimension, which has been used to study more prominent features, and improve performance with a small number of study data and considering different conditions, and using the new feature extraction method and improving the performance of the algorithms, 96% accuracy has been achieved.

This paper reports support vector machine (SVM) based Fault detection and classification in microgrid while considering distortions in voltages and currents, time and frequency series parameters, and differential parameters. For SVM-based Fault classification, the data set is formed by analysing the operation of the standard IEC microgrid model, with and without grid interconnection, under different Fault and non-Fault scenarios. Fault scenarios also include different locations, resistances, and incident angles of Fault. Whereas, for non-Fault scenarios, the variation in load is considered. Voltages and currents from both ends of the distribution line (DL) are sampled at 1920 Hz. The time and frequency series parameters, total harmonic distortion (THD) in current and voltage, and differential parameters are determined. The SVM algorithm uses these parameters to detect and classify Faults. The performance of this developed SVM based algorithm is compared with that of different machine learning algorithms. This comparative analysis reveals that SVM detects and classifies the Faults on the microgrid with an accuracy of over 99.99%. The performance of the proposed method is also tested with 30 dB, 35 dB, and 40 dB noise in the generated data, which represent measurement errors.

One of the critical components in most electromechanical systems are the bearing system. Therefore, a proper condition monitoring method that can classify the type and the severity of electrical machine Faults in different load levels is crucial to avoid unwanted downtime and loss of operation. Non-invasive condition monitoring methods based on electrical signatures of machine in an electromechanical system, are considered as simple and cost-effective approaches for the Fault detection process. In this paper, a deep learning approach based on a combination of temporal convolutions and Long Short Term Memory (LSTM) network is used for Fault diagnosis. The two architectures are both shown to be effective for time-series classification and sequence modeling. Temporal convolutions are shown to be competent in feature extraction for time-series classification; however, they are rarely studied in bearing Fault detection and classification in an electromechanical system. The presented method does not need any preprocessing or predetermined signal transformation, and uses the raw time-series sensor data. In this regard, three different Faults, as inner race, outer race, and balls are considered for validity of the proposed method. The results show that healthy cases can be separated from Faulty cases in different load levels with high accuracy (95.8%).