Fault Detection in Transmission Line by Magnitude and Phase Angle Extraction based on Neuro – Fuzzy Approach

The main aim of this paper is to detection and classifies the various types of ground and line faults in transmission line. A new current decomposition is proposed in order to derive the positive, negative and zero current components. The faults may be an insulation failure of conductors and cables, lightning or accidental faulty operation of any connected equipment. In a transmission line detection a fault is very important since if any fault occurs in finding fault may leads to disoperation of the transmission line protection system. So either a disturbances or steady state variations are called power quality variation. In this paper, a new concept is used for online symmetrical components and phase-angle extraction from high voltage transmission-line faults. This method is based on the fuzzy interference system and the instantaneous power theory. The average and oscillating terms of the powers in the αβ frame are separated by using fuzzy logic controller. After various shunt faults phase angle is estimated at each phase of the line. This approach is very much suitable for online implementation.