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A Novel Hybrid Differential Algorithm for Turn to Turn Fault Detection in Shunt Reactors

Das, Sarasij and Sidhu, Tarlochan S and Zadeh, Mohammad Reza Dadash and Zhang, Zhiying (2017) A Novel Hybrid Differential Algorithm for Turn to Turn Fault Detection in Shunt Reactors. In: IEEE TRANSACTIONS ON POWER DELIVERY, 32 (6). pp. 2537-2545.

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Official URL: http://doi.org/10.1109/TPWRD.2017.2680456

Abstract

Turn to turn faults usually cause smaller changes in the phase currents of a shunt reactor when fewer turns are involved. Designing a sensitive and reliable algorithm for turn to turn fault detection in shunt reactors still remains a challenge. In this paper, a novel hybrid differential algorithm has been proposed to detect turn to turn faults in shunt reactors. The proposed algorithm calculates the difference between normalized negative sequence terminal voltage and normalized negative sequence reactor current phasors. This difference value is used for detecting turn to turn fault in shunt reactors. The proposed algorithm can also identify the faulty phase. This is a significant improvement with respect to the existing negative or zero sequence based methods. The proposed algorithm does not need neural CT. Impedance values of the shunt reactors are also not needed in the calculations. The proposed algorithm can be applied to both solidly and impedance grounded shunt reactors. The performance of the proposed algorithm is evaluated using PSCAD simulations. It is found that the proposed algorithm is sensitive enough to detect lower level turn to turn faults. The proposed algorithm performs satisfactorily during system unbalances, reactor energizations, external faults, off-nominal frequency, and switch onto fault scenarios, etc.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Depositing User: Id for Latest eprints
Date Deposited: 26 Dec 2017 06:04
Last Modified: 26 Dec 2017 06:04
URI: http://eprints.iisc.ac.in/id/eprint/58417

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