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Abstract

This paper presents method of feature subset selection in dynamic stability assessment (DSA) power system using artificial neural networks (ANN). In the application of ANN on DSA power system, feature subset selection aims to reduce the number of training features, cost and memory computer. However, the major challenge is to reduce the number of features but classification rate gets a high accuracy. This paper proposes applying Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS) and Feature Ranking (FR) algorithm to feature subset selection. The effectiveness of the algorithms was tested on the GSO-37bus power system. With the same number of features, the calculation results show that SFS algorithm yielded higher classification rate than FR, SBS algorithm. SFS algorithm yielded the same classification rate as SFFS algorithm.



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Article Details

Issue: Vol 18 No 2 (2015)
Page No.: 15-24
Published: Jun 30, 2015
Section: Engineering and Technology - Research article
DOI: https://doi.org/10.32508/stdj.v18i2.1054

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Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Nguyen, A., Nguyen, A., & Phan, B. (2015). Feature subset selection in dynamic stability assessment power system using artificial neural networks. Science and Technology Development Journal, 18(2), 15-24. https://doi.org/https://doi.org/10.32508/stdj.v18i2.1054

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