A neural space vector fault location for parallel double-circuit distribution lines

Abstract

A new approach to fault location for parallel double-circuit distribution power lines is presented. This approach uses the Clarke–Concordia transformation and an artificial neural network based learning algorithm. The α, β, 0 components of double line currents resulting from the Clarke–Concordia transformation are used to characterize different states of the system. The neural network is trained to map the non-linear relationship existing between fault location and characteristic eigenvalue. The proposed approach is able to identify and to locate different types of faults such as: phase-to-earth, phase-to-phase, two-phase-to-earth and three-phase. Using the eigenvalue as neural network inputs the proposed algorithm locates the fault distance. Results are presented which shows the effectiveness of the proposed algorithm for a correct fault location on a parallel double-circuit distribution line.