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A Data-Mining Model for Protection of FACTS-Based Transmission Line

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1 Author(s)
Samantaray, S.R. ; School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, India

This paper presents a data-mining model for fault-zone identification of a flexible ac transmission systems (FACTS)-based transmission line including a thyristor-controlled series compensator (TCSC) and unified power-flow controller (UPFC), using ensemble decision trees. Given the randomness in the ensemble of decision trees stacked inside the random forests model, it provides effective decision on fault-zone identification. Half-cycle postfault current and voltage samples from the fault inception are used as an input vector against target output “1” for the fault after TCSC/UPFC and “ $-$1” for the fault before TCSC/UPFC for fault-zone identification. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network, including noisy environment providing a reliability measure of 99% with faster response time (3/4th cycle from fault inception). The results of the presented approach using the RF model indicate reliable identification of the fault zone in FACTS-based transmission lines.

Published in:

Power Delivery, IEEE Transactions on  (Volume:28 ,  Issue: 2 )

Date of Publication:

April 2013

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