Recognition of Hybrid PQ Disturbances Based on Multi-Resolution S-Transform and Decision Tree  

作　　者：Feng Zhao Di Liao Xiaoqiang Chen Ying Wang 

机构地区：[1]School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou,730070,China [2]Key Lab of Opt-Electronic Technology and Intelligent Control of Ministry of Education,Lanzhou Jiaotong University,Lanzhou,730070,China

出　　处：《Energy Engineering》2023年第5期1133-1148,共16页能源工程（英文）

基　　金：Foundation of China(No.52067013);the Key Natural Science Fund Project of Gansu Provincial Department of Science and Technology(No.21JR7RA280);the Tianyou Innovation Team Science Foundation of Intelligent Power Supply and State Perception for Rail Transit(No.TY202010);the Natural Science Foundation of Gansu Province(No.20JR5RA395).

摘　　要：Aiming at the problems of multiple types of power quality composite disturbances,strong feature correlation and high recognition error rate,a method of power quality composite disturbances identification based on multiresolution S-transform and decision tree was proposed.Firstly,according to IEEE standard,the signal models of seven single power quality disturbances and 17 combined power quality disturbances are given,and the disturbance waveform samples are generated in batches.Then,in order to improve the recognition accuracy,the adjustment factor is introduced to obtain the controllable time-frequency resolution through multi-resolution S-transform time-frequency domain analysis.On this basis,five disturbance time-frequency domain features are extracted,which quantitatively reflect the characteristics of the analyzed power quality disturbance signal,which is less than the traditional method based on S-transform.Finally,three classifiers such as K-nearest neighbor,support vector machine and decision tree algorithm are used to effectively complete the identification of power quality composite disturbances.Simulation results showthat the classification accuracy of decision tree algorithmis higher than that of K-nearest neighbor and support vector machine.Finally,the proposed method is compared with other commonly used recognition algorithms.Experimental results show that the proposedmethod is effective in terms of detection accuracy,especially for combined PQ interference.

关 键 词：Hybrid power quality disturbances disturbances recognition multi-resolution S-transform decision tree 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]