Deep Learning Based Relay for Online Fault Detection, Classification, and Fault Location in a Grid-Connected Microgrid

Abstract

In this article, a maiden attempt have been taken for the online detection of faults, classification of faults, and identification of the fault locations of a grid-connected Micro-grid (MG) system. A deep learning algorithm-based Long Short Term Memory (LSTM) network is proposed, for the first time, for the online detection of faults and their classifications of the considered MG system to overcome the issues that persist in the existing algorithms. Also, a combination of an LSTM network and feed-forward neural network (FFNN) with a back-propagation algorithm (BPA) is proposed to identify the exact locations of the faults since the identification of fault locations is more challenging than fault categorizations. To select a suitable deep learning network with multiple hidden layers for achieving the aforesaid objectives, a rigorous analysis has been done. To study the accuracy of the proposed techniques, different types of faults with different parameters are considered in this paper. An extensive simulation has been done in MATLAB/Simulink platform to study the performance of the system with the proposed techniques. To validate the effectiveness of the proposed techniques, the entire system is implemented in the real-time platform using the OPAL-RT digital simulator. Comparison has also been done for the results obtained using ANN and proposed techniques. The results show that the proposed techniques based on the deep learning network effectively detect, classify, and identify the location of different faults of an MG system with acceptable performances.