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

Bhupendra Kumar, Anamika Yadav


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 threephase
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.


FACTS; TCSC; Fuzzy Logic; DFT; Fault Detection; Fault classification; Power transmission

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