Coordinating fuzzy ART neural networks to improve transmission line fault detection and classification

Nan Zhang, Mladen Kezunovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

This paper demonstrates several uses of Adaptive Resonance Theory (ART) based neural network (NN) algorithm combined with Fuzzy K-NN decision rule for fault detection and classification on transmission lines. To deal with the large input data set covering system-wide fault scenarios and improve the overall accuracy, three Fuzzy ART neural networks are proposed and coordinated for different tasks. The performance of improved scheme is compared with the previous development based on the simulation using a typical power system model. The speed and accuracy of detecting continuous signals during the fault is also evaluated. Simulation results confirm the improvement benefits when compared with the previous implementation.

Original languageEnglish (US)
Title of host publication2005 IEEE Power Engineering Society General Meeting
Pages734-740
Number of pages7
Volume1
StatePublished - Oct 31 2005
Event2005 IEEE Power Engineering Society General Meeting - San Francisco, CA, United States
Duration: Jun 12 2005Jun 16 2005

Other

Other2005 IEEE Power Engineering Society General Meeting
CountryUnited States
CitySan Francisco, CA
Period6/12/056/16/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Zhang, N., & Kezunovic, M. (2005). Coordinating fuzzy ART neural networks to improve transmission line fault detection and classification. In 2005 IEEE Power Engineering Society General Meeting (Vol. 1, pp. 734-740)