High Impedance Fault Detection Using Artificial Neural Network Techniques Texas A&M Univ

Format:

Conference/Event

Author/Creator:

Fernando, Susith R., author.

Conference Name:

27th Intersociety Energy Conversion Engineering Conference (1992) (1992-08-03 : San Diego, California, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 1992

Summary:

Artificial Neural Network(ANN) techniques are used to develop a system to detect High Impedance Faults(HIFs) in electric power distribution lines. Encouraging results were observed with a simple Multi-layer Perceptron(MLP) trained with the backpropagation learning algorithm. Although the results are not significantly better than those reported with other algorithmic approaches, ANN techniques have potential advantages over the other approaches; namely, ability to train the system easily to accommodate different feeder characteristics, ability to adapt and so become a better detector with experience and better fault tolerance. When these features are incorporated, the system is expected to perform better than existing systems.The system we developed for the current phase, the training strategies used, the tests conducted and the results obtained are discussed in this paper. Also background discussions on existing HIF detection techniques, and ANN techniques can be found in this paper. A feature extraction method based on fuzzy logic and a bin structure is discussed

Notes:

Vendor supplied data

Publisher Number:

929103

Access Restriction:

Restricted for use by site license