An artificial neural network based trouble call analysis

Author

Lu, C.N. ; Tsay, M.T. ; Hwang, Y.J. ; Lin, Y.C.

Author_Institution

Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan

Volume

9

Issue

3

fYear

1994

fDate

7/1/1994 12:00:00 AM

Firstpage

1663

Lastpage

1668

Abstract

The use of trouble call information for handling service interruption events that exist between primary feeders and customers is discussed in this paper. Artificial neural networks are used for fast pattern recognition and classification of trouble calls such that the time and effort required for service restoration can be reduced. A backpropagation network is chosen as the neural network model. This proposed trouble call analysis system is embedded in an integrated automated mapping, facilities management and geographic information system environment

Keywords

backpropagation; distribution networks; geographic information systems; neural nets; pattern recognition; power engineering computing; power system restoration; artificial neural network; automated mapping; backpropagation network; distribution system; facilities management; fast pattern recognition; geographic information system environment; primary feeders; service interruption events; service restoration; trouble call analysis; trouble call information; trouble calls classification; Artificial neural networks; Circuit faults; Diagnostic expert systems; Dispatching; Fault diagnosis; Fault location; Monitoring; Power system restoration; Power transmission lines; SCADA systems;

fLanguage

English

Journal_Title

Power Delivery, IEEE Transactions on

Publisher

ieee

ISSN

0885-8977

Type

jour

DOI

10.1109/61.311196

Filename

311196