Metodologia para detecção e localização de áreas de defeitos de alta impedância com a presença da geração distribuída

Abstract

This work proposes the development of numerical models and methods, based on artificial neural networks, for the detection and localization of high impedance faults in distribution systems. In parallel, the efficiency is also evaluated using different types of measurement data techniques in the performance of the method, which is implemented through two steps. The first step consists in the adaptation of an existing program for calculation of faults, aiming to generate randomly several types of faults, as well as their location. The faults calculation methodology was developed using current injection equations in rectangular coordinates. In this program the models of load variation with the voltage during the faults and a variety of conventional and unconventional models for distributed generation, are considered. Next, a method based on artificial neural networks is developed and implemented for the detection and identification of faults, as well as to estimate the fault location within a distribution system. The neural network inputs are modules and angles of the voltages and currents of the electrical system, obtained from the PMUs and / or IEDs. The outputs of the neural network correspond to the detection and location of faults. The proposed method was developed in MatLab® environment and tested in some IEEE systems and in a real system in order to evaluate its efficiency. The results obtained from the studies was presented in the form of tables and graphs with their respective accuracy, numbers of neurons and the different configurations adopted.