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

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. 2005 IEEE.

name of conference

• IEEE Power Engineering Society General Meeting, 2005

authors

• Kezunovic, Mladen

published proceedings

• 2005 IEEE POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS, 1-3

author list (cited authors)

• Zhang, N., & Kezunovic, M.

citation count

• 21

complete list of authors

• Zhang, N||Kezunovic, M

publication date

• January 2005

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Power and Energy Society General Meeting  Journal

keywords

• Adaptive Resonance Theory
• Fuzzy Logic
• Neural Networks
• Pattern Recognition
• Power System Faults
• Power System Protection
• Protective Relays

Digital Object Identifier (DOI)

• 10.1109/pes.2005.1489373

International Standard Book Number (ISBN) 10

• 0-7803-9156-X

International Standard Book Number (ISBN) 13

• 9780780391567

start page

• 734

end page

• 740

volume

• 1

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fpes.2005.1489373