Fault analysis and detection on multiple points in transmission line through Mho relay and its data prediction through LSTM technique

Power systems are becoming increasingly complex due to the integration of several power electronic devices. Protection of such systems and augmentation of reliability and stability depend on limiting the fault currents. Analysis of faults is essential for the selection of appropriate relays and other safety systems. In this research work fault analysis, fault detection, and data prediction of long transmission lines are discussed. Different parameters are analyzed at each point of distance using MATLAB/Simulink for long transmission lines. Through these results, it is concluded that with the increase in distance, the parameters change, so the detection response of the Mho relay also varies. Moreover, in this research work, the Mho relay response at different points is tested through Simulink software for long transmission lines. As it is concluded that manually it is very difficult to test the response of Mho relay at each point, the artificial intelligence technique of long short-term memory (LSTM) is used for the prediction of the data for the next long transmission line. Lastly, in this work, a long short-term memory network is used to predict the parameters. The LSTM is then used to find the predicted data of subsequent transmission line from previous data. The artificial intelligence technique successfully utilized for the detection of long transmission line fault.