Real-time and off-line transmission line fault classification using neural networks Academic Article

abstract

• This paper is concerned with application of Neural Networks (NNs) to fault classification for both the real-time applications such as protective relaying of transmission lines and the off-line applications such as post-mortem study of fault events recorded with Digital Fault Recorders (DFRs). A supervised learning NN of the same type is utilized for both applications. It has been demonstrated that the NN approach reaches performance of the existing techniques in both application areas and yet shows some additional benefits.

authors

• Kezunovic, Mladen

published proceedings

• ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS

author list (cited authors)

• Kezunovic, M., Rikalo, I., & Sobajic, D. J.

complete list of authors

• Kezunovic, M||Rikalo, I||Sobajic, DJ

publication date

• December 1996

published in

• International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications  Journal

keywords

• Emtp Simulations
• Fault Classification
• Neural Networks
• Supervised Clustering

start page

• 57

end page

• 63

volume

• 4

issue

• 1