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.
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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Science and Technology Development Journal (STDJ) (1859-0128) is the official journal of Viet Nam National University Ho Chi Minh City, Viet Nam, published by Viet Nam National University Ho Chi Minh City, Viet Nam. Science & Technology Development Journal is a multiple discipline science journal covering from natural science, engineering & technology, humanities, art, laws, economics, earth science, environment, social sciences and health sciences.