A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data

Abstract

Electric power grids are always affected by numerous unexpected faults. Occurrence of these faults will have a negative impact on network availability and reliability indices of the network. But the indicators of reliability and quality of electrical energy in the network can be augmented by locating the fault in the shortest time. Special features of distribution networks such as load and network imbalance, existence of different types of load with different connections, existence of multiphase branches, effects related to different conductors, capacitive effects of distribution lines, and limited numbers of measuring devices complicate the process of fault localization in distribution networks. On the other hand, increasing penetration of distributed generation units has caused conventional methods of fault localization. Therefore, it is mandatory to introduce new methods of fault locating by considering the mentioned features. Hence, in the current study, nonlinear methods are presented for identifying the location of ground faults in the power distribution network with the help of voltage phasor measurement at different network buses by the D-PMU phased distribution unit. In the first technique, the genetic optimization algorithms and particle swarm optimization for nonlinear modeling of fault position along the distribution line have been utilized for different single-phase, two-phase, and three-phase faults, and in the second technique, neural fuzzy network training has been proposed by different phasor measurement devices. In this case, it is enough to access the phase information of the network bus voltage. In order to show the effectiveness of the proposed algorithms, a 9-bus system is defined by MATLAB software and also defining different line lengths and line characteristics in different buses. Moreover, after applying single-phase, two-phase, and three-phase faults, as well as presenting the results, fault localization is detected in the shortest time.