A Fuzzy Logic System to Detect and Classify Faults for Laboratory Prototype Model of TCSC Compensated Transmission Line

In this paper, an expert system-based fault detection and classification scheme is developed for a laboratory prototype model of TCSC compensated long transmission line (thyristor controlled series compensator). The equivalent model of laboratory prototype system is simulated in MATLAB Simulink. An expert system based on fuzzy logic is developed by using three-phase voltage and current signals from single end measurements. Obtained voltage and current signals are pre-processed with Discrete Fourier Transform (DFT) to obtain the fundamental component of these signals. Further zero sequence current and obtained fundamental voltage and current signals are used to develop a fuzzy inference system (FIS) for shunt fault detection and classification task. There are three different FISs developed for three individual phases of the transmission system and one FIS is developed for zero sequence current signal, which provides ground involvement information. The combined binary output of the developed four FISs provides fault classification. The performance of the developed FISs is rigorously tested with the variation of different fault parameters, and different location of the TCSC. The simulated results indicate that the proposed scheme performance is reliable in its zone of protection.