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

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.