Recognition Method for Multi-scale Sparse Power Quality Disturbance

被引：0
|
作者：
Zhu Y. [1 ]
Wu Z. [1 ]
Gao Y. [1 ]
Hou Y. [1 ]
Liu Z. [1 ]
机构：
[1] School of Electrical Engineering, Southwest Jiaotong University, Chengdu
来源：
Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University | 2020年 / 55卷 / 01期
关键词：
Compressed sensing; Cross-entropy optimization; Deep belief network; Disturbance recognition; Power quality;
D O I：
10.3969/j.issn.0258-2724.20180606
中图分类号：
学科分类号：
摘要：
In the traditional power quality disturbance recognition, there is a large amount of data and disturbance characteristics are dependent on subjective selection. To deal with these problems, a recognition method for multi-scale sparse power quality disturbance is proposed. Firstly, a multi-scale sparse model for power quality signal is constructed. Through the stationary wavelet transform (SWT) for the disturbance signal, its low and high frequency information is obtained. Then by compressed sampling for the disturbance signal, the dimension reduction data are obtained. Further, sparse coefficients calculated by orthogonal matching pursuit (OMP) algorithm constitute a sparse vector, which is directly inputted into the deep belief network to achieve intelligent disturbance classification. Meanwhile, to improve the recognition rate, cross-entropy algorithm is applied to find the optimal parameters such as the number of hidden layers and learning rate. Finally, in order to verify the effectiveness of the proposed method, a large number of simulation tests were performed for several typical single disturbances and mixed disturbances. The simulation results demonstrate that in the ideal environment the averaged recognition rate of this method for seven typical single disturbances and thirteen mixed disturbances is 99.0% and 97.69% respectively, and in noisy environment at least 96.71% and 94.62% respectively, which shows that the proposed method has a desirable performance in disturbance identification. © 2020, Editorial Department of Journal of Southwest Jiaotong University. All right reserved.
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页码：18 / 26
页数：8
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