Combination of MW and Deep Neural Network Based Fault Location Identification for Multi-Terminal Transmission Systems
Y Rao (Koneru Lakshmaiah Education Foundation, Vaddeswaram, India); P Srinivas Varma (Koneru Lakshmaiah Education Foundation Guntur, India)
It is essential to identify the fault location in multi terminal transmission systems with respect to end terminals of the transmission lines, to provide basic information to design protective components in power system network. In this paper post fault currents are carried out at different terminals of IEEE-9 Bus system using MATLAB simulation tool. These currents have been processed through Morphological Wavelet (MW) filters to discriminate the different faults at various inception angles and fault resistances. Opening, closing, Morphological Closing and Opening Average RMS Value (COAVR) are selected as filters for morphological operations. The COAVR outputs are applied as inputs for Deep Neural Network (DNN) to identify the faulty line, type of fault and exact fault location with respect to one end of the transmission line.
Journal: International Journal of Simulation- Systems, Science and Technology- IJSSST V20
Published: Jan 30, 2019