Application of Wavelets in Power System Protection

Abstract

Power system protection is an important issue that should be handled in order to keep the electrical power system dependability, security and reliability. In recent years, Discrete Wavelet Transforms (DWTs) have been widely acknowledged as one of the most promising developments in the field of electric power protection. Wavelet transform consists of a set of families that differ than each other in the wavelet function and scaling function, but they agreed in analyzing the data in both time scale and frequency scale. In this thesis, four DWTs families are used for protection of two power system equipment (transmission lines and transformers) and a study has been carried to evaluate the most effective family that can be used for protection of the above equipment. The purpose of using the DWT analysis for transmission line protection is to determine the fault location and fault type. While, the DWT analysis is used for transformer protection to discriminate between the internal fault currents and the magnetizing inrush currents in the primary winding at the instance of transformer switching. A hybrid system of both DWT and Artificial Neural Network (ANN) is used for that purpose due to the great ability of ANN to deal with classification systems. Simulation results revealed satisfactory performance of the selected DWT family for both transmission line protection and transformer protection