Power System Security Margin Prediction Using Radial Basis Function Networks

Date
1997-06-27
Authors
Zhou, Guozhong
McCalley, James
McCalley, James
Honavar, Vasant
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Altmetrics
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Computer Science
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Computer Science
Abstract

This paper presents a method to predict the postcontingency security margin using radial basis function (RBF) networks. A genetic algorithm-based feature selection tool is developed to obtain the most predictive attributes for use in RBF networks. The proposed method is applied to a thermal overload problem for demonstration. Simulation results show that the proposed method gives satisfactory results and the running time decreases by a factor of 10 compared with using multilayer perceptrons.

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