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

被引：0

作者：

Zhou C. ^{[1
]}

Shao Z. ^{[1
]}

Chen F. ^{[1
]}

Zhang Y. ^{[1
]}

机构：

[1] Fujian Smart Electrical Engineering Technology Research Center (Fuzhou University), Fujian Province, Fuzhou

来源：

Dianwang Jishu/Power System Technology | 2023年 / 47卷 / 09期

基金：

中国国家自然科学基金;

关键词：

cuckoo search; feature selection; multi-indicator fusion; power quality disturbances classification;

D O I：

10.13335/j.1000-3673.pst.2022.1088

中图分类号：

学科分类号：

摘要：

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.

引用

页码：3873 / 3883

页数：10

共 26 条

[21] LAI Xuefang, HE Xingshi, Method based on minimum redundancy and maximum separability for feature selection[J], Computer Engineering and Applications, 53, 12, pp. 70-75, (2017)
[22] Hanchuan PENG, LONG Fuhui, DING C．, Feature selection based on mutual information criteria of max-dependency，max-relevance，and min-redundancy[J], IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 8, pp. 1226-1238, (2005)
[23] (2013)
[24] HUANG N E，, SHEN Zheng, LONG S R, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J], Proceedings of the Royal Society A：Mathematical Physical and Engineering Sciences, 454, 1971, pp. 903-995, (1998)
[25] YANG Weixi, LIU Yong, SHU Qin, A short-term load forecasting model based on CEEMD[J], Power System Technology, 46, 9, pp. 3615-3622, (2022)
[26] SALESI S, COSMA G．, A novel extended binary cuckoo search algorithm for feature selection[C], Proceedings of the 2nd International Conference on Knowledge Engineering and Applications, pp. 6-12, (2017)

← 1 2 3 →