Enhanced Fault Detection and Classification in AC Microgrids Through a Combination of Data Processing Techniques and Deep Neural Networks

This paper introduces an innovative method for the intelligent protection of AC microgrids that incorporate renewable energy sources and electric vehicle charging stations. To extract relevant features, current signals from both sides of the distribution line are sampled. Subsequently, the differential current is calculated, and the resultant signals are processed using Compressed Sensing Theory and Variational Mode Decomposition to extract key features. These extracted features serve as input data for training the proposed wide and deep learning model. The proposed method was evaluated on a microgrid that incorporated electric vehicle chargers and wind turbines. The results indicate that this approach can effectively identify and categorize different types of faults in AC microgrids. Moreover, it demonstrates stable and dependable performance in the face of typical transients, and its accuracy is not influenced by uncertainties in the microgrid topology.