A Neural Network Based Electrical Loss Prediction of Bare Overhead ACSR Conductors

Author

Liu, Fang ; Findlay, Raymond D. ; Song, F. Qiang

Author_Institution

McMaster Univ., Hamilton, Ont.

Volume

2

fYear

2006

fDate

Aug. 30 2006-Sept. 1 2006

Firstpage

392

Lastpage

395

Abstract

Due to the current resource crisis, reliable electrical loss analysis and prediction attract more attention than ever for efficient power system management. This paper successfully predicts the electrical loss of ACSR with actual daily load variations using an accurate and effective neural network model, which is validated by visual comparison and strict statistical criterion. Since electrical loss for ACSR is closely related with ac resistance, case study is performed on a typical ACSR to investigate and simulate its ac resistance properties

Keywords

fault location; losses; neural nets; overhead line conductors; power system analysis computing; power system faults; power system management; power system reliability; ac resistance property; bare overhead ACSR conductor; neural network based electrical loss prediction; power system management; reliable electrical loss analysis; Aluminum; Computational modeling; Conductors; Electric resistance; Load management; Neural networks; Power system reliability; Predictive models; Steel; Temperature;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on

Conference_Location

Beijing

Print_ISBN

0-7695-2616-0

Type

conf

DOI

10.1109/ICICIC.2006.205

Filename

1692008