EPEC 2010 , 2010-08-25

Title : ( Static Security Assessment Using Radial Basis Function Neural Networks Based on Growing and Pruning Method )

Authors: Habib Rajabi Mashhadi , Seyed Dawood Seyed Javan , M Rouhani ,

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Power system security is one of the major concerns in recent years due to the deregulation of power systems which are forced to operate under stressed operating conditions. This paper presents a novel method based on growing and pruning training algorithm using radial basis function neural network (GPRBFNN) and winner-take-all neural network (WTA) to examine whether the power system is secure under steady-state operating conditions. Hidden layer neurons have been selected with the proposed algorithm which has the advantage of being able to automatically choose optimal centers and distances. A feature selection technique-based class separability index and correlation coefficient has been employed to identify the inputs for the GPRBF network. The advantages of this method are simplicity of algorithm and high accuracy in classification. The effectiveness of the proposed approach has been demonstrated on IEEE 14-bus and IEEE 30-bus systems.

Keywords

, Static security assessment, Radial basis function neural network, Class separability index, Correlation coefficient, Winner-take-all neural
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@inproceedings{paperid:1020813,
author = {Rajabi Mashhadi, Habib and Seyed Javan, Seyed Dawood and M Rouhani},
title = {Static Security Assessment Using Radial Basis Function Neural Networks Based on Growing and Pruning Method},
booktitle = {EPEC 2010},
year = {2010},
location = {Halifax, IRAN},
keywords = {Static security assessment; Radial basis function neural network; Class separability index; Correlation coefficient; Winner-take-all neural network.},
}

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%0 Conference Proceedings
%T Static Security Assessment Using Radial Basis Function Neural Networks Based on Growing and Pruning Method
%A Rajabi Mashhadi, Habib
%A Seyed Javan, Seyed Dawood
%A M Rouhani
%J EPEC 2010
%D 2010

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