An improved neural network algorithm for classifying the transmission line faults Conference Paper

abstract

• This study introduces a new concept of artificial intelligence based algorithm for classifying the faults in power system networks. This classification identifies the exact type and zone of the fault. The algorithm is based on unique type of neural network specially developed to deal with large set of highly dimensional input data. An improvement of the algorithm is proposed by implementing various steps of input signal preprocessing, through the selection of parameters for analog filtering, and values for the data window and sampling frequency. In addition, an advanced technique for classification of the test patterns is discussed and the main advantages comparing to previously used nearest neighbor classifier are shown.

name of conference

• 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309)

authors

• Kezunovic, Mladen

published proceedings

• 2002 IEEE POWER ENGINEERING SOCIETY WINTER MEETING, VOLS 1 AND 2, CONFERENCE PROCEEDINGS

author list (cited authors)

• Vasilic, S., & Kezunovic, M.

citation count

• 27

complete list of authors

• Vasilic, S||Kezunovic, M

publication date

• January 2002

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Clustering Methods
• Electromagnetic Transients
• Neural Networks
• Pattern Classification
• Power System Faults
• Protective Relaying
• Testing
• Training.

Digital Object Identifier (DOI)

• 10.1109/pesw.2002.985139

International Standard Book Number (ISBN) 10

• 0-7803-7322-7

start page

• 918

end page

• 923

volume

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fpesw.2002.985139