Feature selection by separability assessment of input spaces for transient stability classification based on neural networks

Abstract

Power system transient-stability assessment based on neural networks can usually be treated as a two-pattern classification problem separating the stable class from the unstable class. In such a classification problem, the feature extraction and selection is the first important task to be carried out. A new approach of feature selection is presented using a new separability measure in this paper. Through finding the ‘inconsistent cases’ in a sample set, a separability index of input spaces is defined. Using the defined separability index as criterion, the breadth-first searching technique is employed to find the minimal or optimal subsets of the initial feature set. The numerical results based on extensive data obtained for the 10-unit 39-bus New England power system demonstrate the effectiveness of the proposed approach in extracting the ‘best combination’ of features for improving the quality of transient-stability classification.