Power System Security Margin Prediction Using Radial Basis Function Networks

Zhou, Guozhong, McCalley, James D. and Honavar, Vasant (1997) Power System Security Margin Prediction Using Radial Basis Function Networks. Technical Report TR97-10, Department of Computer Science, Iowa State University.

Abstract

Power System Security Margin Prediction Using Radial Basis Function Networks
Technical Report ISU-CS-TR 97-10
Guozhong Zhou James D. McCalley
Department of Electrical and Computer Engineering
Iowa State University
Ames, Iowa 50011
Vasant Honavar
Department of Computer Science
Iowa State University
Ames, Iowa 50011
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.
Keywords: Security margin, power systems, radial basis function networks,
genetic algorithms, feature selection
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This paper appears in the Proceedings of the 29th Annual North American
Power Symposium, Oct 13-14, Laramie, Wyoming.


Subjects:
All uncategorized technical reports


ID code:
00000156
Deposited by:
Staff Account on 30 June 1997





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