Feature Reduction for Power System Transient Stability Assessment Based on Neighborhood Rough Set and Discernibility Matrix

Bingyang Li, Jianmei Xiao and Xihuai Wang
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Bingyang Li: Department of Electrical Engineering, Shanghai Maritime University, Shanghai 201306, China
Jianmei Xiao: Department of Electrical Engineering, Shanghai Maritime University, Shanghai 201306, China
Xihuai Wang: Department of Electrical Engineering, Shanghai Maritime University, Shanghai 201306, China

Energies, 2018, vol. 11, issue 1, 1-19

Abstract: In machine learning-based transient stability assessment (TSA) problems, the characteristics of the selected features have a significant impact on the performance of classifiers. Due to the high dimensionality of TSA problems, redundancies usually exist in the original feature space, which will deteriorate the performance of classification. To effectively eliminate redundancies and obtain the optimal feature set, a new feature reduction method based on neighborhood rough set and discernibility matrix is proposed in this paper. First, 32 features are selected to structure the initial feature set based on system principle. An evaluation index based on neighborhood rough set theory is used to characterize the separability of classification problems in the specified feature space. By constructing the discernibility matrix of input features, a feature selection strategy is designed to find the optimal feature set. Finally, comparative experiments based on the proposed feature reduction method and several common feature reduction techniques used in TSA are applied to the New England 39 bus system and Australian simplified 14 generators system. The experimental results illustrate the effectiveness of the proposed feature reduction method.

Keywords: feature selection; transient stability assessment (TSA); neighborhood rough set; power system security; discernibility matrix (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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