Fault Classification and Its Identification in Overhead Transmission Lines Using Artificial Neural Networks

In modern power systems, fault classification and placement are critical for improving protection schemes, service reliability, and reducing line outages. However, due to the mutual coupling effect, it confronts usual issues in fault identification in double-circuit lines. Although several methods have been shown to be accurate in locating double-circuit line faults, they have limitations in certain situations such as a lack of synchronization between the measuring ends, identification of the type of fault, data before and after a fault, three-phase faults, and so on. This study offers an artificial neural network technique that uses a probabilistic neural network (PNN) for fault classification and a generalized regression neural network (GRNN) for fault localization to address some of these challenges. To categorize and detect the fault, the proposed technique leverages the fundamental current phasor magnitudes obtained at both circuit endpoints of the double circuit. To validate the proposed method, a 200 km overhead transmission line is simulated using MATLAB/SIMULNK for all sorts of faults by varying location of the fault, resistance of the fault, and inception angle of the fault.