Deep Learning–Based Fault Detection and Classification in AC Microgrids using CNNs

Abstract

Since there are issues with bidirectional power flow, fault current levels are extremely low, and operating conditions are dynamic in nature, with high challenges in terms of fault detection and classification with distributed energy resources in AC microgrid. To maintain system reliability and assure promptness in fault detection and response, it is extremely important to properly identify common faults like LG fault, LL fault, LLG fault, and three-phase faults. A new method based on deep learning and a deep learning method based on a convolutional neural network are presented in this paper. With this method, there would not be a need to specifically extract features. Since a large amount of data was simulated in terms of a modified 7-bus AC system created in RTDS software and tested in different fault conditions and operating conditions, a large amount of data was generated. The model finally uses a dense layer and a SoftMax classification. A comparison was made on its performance of the conventional machine learning algorithms such as Multilayer Perceptron Classifier, Decision Tree Classifier, Naïve Bayes Classifier, and the Support Classifier Machine. In the simulation, the performance of the proposed model was found to be superior to the existing methods with an accuracy of up to 99%, thereby clearly distinguishing between symmetry and asymmetry through the analysis of the confusion matrix used in the model.