Fuzzy ART neural network algorithm for classifying the power system faults Academic Article

abstract

• This paper introduces advanced pattern recognition algorithm for classifying the transmission line faults, based on combined use of neural network and fuzzy logic. The approach utilizes self-organized, supervised Adaptive Resonance Theory (ART) neural network with fuzzy decision rule applied on neural network outputs to improve algorithm selectivity for a variety of real events not necessarily anticipated during training. Tuning of input signal preprocessing steps and enhanced supervised learning are implemented, and their influence on the algorithm classification capability is investigated. Simulation results show improved algorithm recognition capabilities when compared to a previous version of ART algorithm for each of the implemented scenarios. 2005 IEEE.

authors

• Kezunovic, Mladen

published proceedings

• IEEE TRANSACTIONS ON POWER DELIVERY

author list (cited authors)

• Vasilic, S., & Kezunovic, M.

citation count

• 100

complete list of authors

• Vasilic, S||Kezunovic, M

publication date

• April 2005

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Adaptive Resonance Theory
• Clustering Methods
• Fuzzy Logic
• Learning Systems
• Neural Networks
• Pattern Classification
• Power System Faults
• Protective Relaying
• Testing
• Training

Digital Object Identifier (DOI)

• 10.1109/TPWRD.2004.834676

start page

• 1306

end page

• 1314

volume

• 20

issue

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Ftpwrd.2004.834676