Six Phase to Ground Fault Detection and Classification of Transmission Line using ANN

Abstract

The six phase transmission line has received considerable attention in recent times as an alternative to the three phase transmission, since it possess several advantages over three phase transmission line. Six-phase transmission line can provide same power transfer capability as three phase on a smaller right-ofway, for the same electric field and audible noise criteria, with smaller structures and reduced overall cost. A total of 120 faults can occur in six phase transmission line. The design of adequate protective scheme is essential for the protection of six phase transmission line. This paper reports the application of Artificial Neural Network for protection of six phase transmission line against six phase to ground faults, which has not been reported yet to the best of the knowledge of the authors. Effects of variations in the fault inception angle (Φi), fault resistance (Rf), distance to fault (Lf) have been studied broadly on the performance of the neural network based protection scheme for six phase to ground fault type. Six phase transmission line is modeled using the Simulink® and Simpowersystem® toolboxes of MATLAB®7.01. The algorithm employs the fundamental components of voltage and current signals. The results indicate the suitability of proposed technique and its adaptability to changing system conditions. The simulation results of ANN based fault detector and classifier indicate that algorithm correctly detects and classifies the six phase to ground fault.