AUTHOR=Gogula Vyshnavi , Edward Belwin 

TITLE=Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1101049

DOI=10.3389/fenrg.2023.1101049

ISSN=2296-598X

ABSTRACT=High Impedance Fault (HIF) detection in a solar photovoltaic (PV) and wind generator integrated power system is described in this paper using WT and a neural network with radial basis function (NNRBF). For this paper, the integration of solar PV and wind systems was modeled in a MATLAB/Simulink environment to create an IEEE 13-bus system. Microgrids (MG’s) are mostly powered by renewable energy. Uncertainty about renewables has shifted attention to ensuring a steady supply and long-term viability. It has been addressed in the paper whether or not a small-scale distant end-source connection may be made at the terminal of a radial distribution feeder. Increases in the number of connected distributed generators (DGs) have significant implications for the power system's design and operation, particularly in terms of the protection strategy. Connecting DG units to a distribution network, however, change the nature of the system so that it is no longer radial, resulting in less cooperation between network protection devices and negative effects on the conventional methods of fault localization. Overhead distribution system faults are simulated in Matlab/Simulink, and the technique is rigorously validated across a wide range of system situations. It has been shown through simulations that the proposed method can be relied upon to successfully and dependably protect high impedance faults (Hi-Z).