Fault Classification using Deep Learning in a Grid-Connected Photovoltaic Systems

PV systems are prone to failure owing to aging and external/environmental factors. These failures can affect a range of system components, such as PV modules, connecting lines, and converters/inverters, re-sulting in decreased efficiency, performance, and even system failure. As a result, problem detection and diag-nosis (FDD) is an important issue in high-efficiency grid-connected PV systems. Deep learning techniques are the most well-known data-driven methodologies. The biggest advantage of deep learning algorithms, in diagnosis, are learning effectiveness, intelligent FDD becomes more effective. This paper therefore presents a comparative study of FDD based deep learning. The techniques include the Convolutional Neural Network (CNN) and Long Short Time Memory (LSTM). Finally, the FDD based frameworks are implemented using simulated PV data. The diagnosis results show that the CNN and LSTM-based fault diagnosis methods are able to detect and diagnose faults under different operating modes.