Locating faults in smart grids using neuro-fuzzy networks

Document Type

Conference Proceeding

Publication Date

10-1-2019

Publication Title

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volume

2019-October

First Page

3281

Last Page

3286

Abstract

Smart grids aim to move the energy industry into a new era of availability, quality, reliability, and efficiency of power at generation, transmission and distribution levels. Transmission lines, like the arteries of smart grids, play an important role in delivering high-quality power from the generation units to the consumers. However, because of their vast geographical spread, they are always exposed to different threats. This paper proposes a computational intelligence method for increasing the protection of the transmission lines in smart grids. Three-phase current measurements of only one side of the faulty transmission line have been collected and passed through a signal processing module to extract novel informative features from the transient current signals generated due to the fault occurrence. Obtained features are then fed to the fault location algorithms to construct predictive models including adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), and artificial neural networks (ANN) to estimate the exact location of the fault. Multiple scenarios have been simulated on the IEEE 14-bus system, and the attained results validate the superiority of the ANFIS over the other methods.

DOI

10.1109/SMC.2019.8913912

ISSN

1062922X

ISBN

9781728145693

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