A novel fault detection and classification technique for double circuit transmission line using Artificial Neural Network

Abstract

Protection and security of double circuit transmission lines is a challenging task. This paper presents an auspice technique predicated on the high-frequency transients. These transients are engendered by the fault to cover virtually the entire length of double circuit transmission lines. For this purpose, felicitously designed high frequency line traps are installed at terminals of the protected transmission line, and the Artificial Neural Network with suitable number of Neurons is used to identify internal and external faults predicated on the frequency spectrum of the current and voltage signals decomposed by Artificial Neural Network logic. Extensive simulation studies designate that the proposed approach is well capable of discriminating between the internal and external faults and provides a secure, very fast, effective and efficient protection technique. The simulation model is done in the MATLAB simulink for system analysis.