Feature Selections for Power Quality Disturbance Signals With Multi-indicator Fusion

There are redundancy and poor separation ability in the power quality disturbance feature sets, which leads to the low classification accuracy of the power quality disturbance signals. Aiming at this problem, a feature selection for the power quality disturbance signals is proposed. First, the Hilbert-Huang transform is used to extract the frequency domain features, and the set of all features of power quality disturbance signals is constructed. Then, the rules of the feature subset selection are constructed based on the indexes of the intersection degree, the redundancy degree and the separation degree, and the selected feature subsets are obtained by the improved cuckoo search method. After that, the cost factor is defined based on the subset dimension and the classification accuracy of the feature subset, so as to evaluate the performance of the feature subset in different dimensions, and then the feature subset with the lowest cost factor is selected as the optimal feature subset. Finally, the optimal feature subset is used to train the classification model and classify the power quality disturbance signals. The simulation results show that the proposed feature selection has better performance in obtaining a subset of features with smaller dimensions and in classifying the power quality disturbance signals. © 2023 Power System Technology Press. All rights reserved.