Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM

Abstract

A modern electric power system integrated with advanced technologies such as sensors and smart meters is referred to as a smart grids, aimed at enhancing electrical power delivery efficiency and reliability. However, fault location and prediction can become challenging when dynamic fault currents from renewable energy sources are present. To address these challenges, three unique deep learning models that make use of Deep Neural Networks (DNN) have been proposed. CNN, LSTM, and Hybrid CNN-LSTM are deep learning models. Line faulty identification (LF), fault classification (FC), and fault location estimate (FL) are the subjects on which they concentrate. These models analyze data gathered both pre and post faults occur in order to enhance decision making. Signals including the voltage and current were fed into these models from many different locations across the test networks. Once the 1D CNN has extracted characteristics from the gathered signals, LSTM uses these features to make accurate estimations and identify faults. Complex data are compatible with this method in terms of optimal outcomes. Using training and testing data from transmission line failure simulations, the proposed approaches were evaluated on the IEEE 6-bus and IEEE 9-bus systems. The tests encompassed a range of fault classes, locations, and ground fault resistances at various locations. Distributed Generator (DG) resources were additionally included in the system architecture and changes in the topology of the networks were considered in terms of location and number of DG resources. The results demonstrated that the proposed algorithms outperformed contemporary technologies in terms of detection, classification, and location accuracy. They demonstrated high accuracy and robustness in their performance.