A PAES based optimization of RBF networks to predict overhead feeder failures

被引：0

作者：

Cochenour, G ^{[1
]}

Simon, J ^{[1
]}

Nag, S ^{[1
]}

Odeh, O ^{[1
]}

Poupe, J ^{[1
]}

Pahwa, A ^{[1
]}

机构：

[1] Kansas State Univ, Dept Elect & Comp Engn, Manhattan, KS 66506 USA

来源：

PROCEEDINGS OF THE 8TH JOINT CONFERENCE ON INFORMATION SCIENCES, VOLS 1-3 | 2005年

关键词：

Pareto Archive Evolutionary Strategy; radial basis function;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper details a method for the multi-objective optimization of radial basis function (RBF) networks to predict failures in an overhead power distribution system. The error and number of kernels are both minimized using the Pareto Archive Evolutionary Strategy. The networks are trained to predict the number of failures based upon daily weather data and outage histories from a Midwestern utility. The results indicate that this method is capable of predicting the daily number of failures.

引用

页码：1726 / 1729

页数：4

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