DETECTION AND CLASSIFICATION OF LINE FAULTS ON POWER DISTRIBUTION-SYSTEMS USING NEURAL NETWORKS Conference Paper

abstract

• This paper presents a new neural network approach based on a clustering algorithm to detect and classify line faults in a power distribution system. A robust features preprocessing procedure is discussed which extracts meaningful features from current wave forms to serve as a reduced set of inputs to the neural network. Lastly, results are given from studies that were conducted to determine the optimal order of presentation of the training feature patterns and the set of features that are necessary for the neural network to perform arcing identification.

name of conference

• Proceedings of 36th Midwest Symposium on Circuits and Systems

authors

• Butler-Purry, Karen

published proceedings

• PROCEEDINGS OF THE 36TH MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1 AND 2

author list (cited authors)

• BUTLER, K. L., & MOMOH, J. A.

citation count

• 12

complete list of authors

• BUTLER, KL||MOMOH, JA

publication date

• January 1993

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• 46 Information And Computing Sciences
• 4611 Machine Learning
• Neurosciences

Digital Object Identifier (DOI)

• 10.1109/MWSCAS.1993.343033

International Standard Book Number (ISBN) 10

• 0-7803-1760-2

start page

• 368

end page

• 371

volume

• 1

URL

• http://dx.doi.org/10.1109/mwscas.1993.343033