Power quality disturbance classification of one-dimensional convolutional neural networks based on feature fusion

被引：0

作者：

Wang W. ^{[1
]}

Zhang B. ^{[1
]}

Zeng W. ^{[1
]}

Dong R. ^{[1
]}

Zheng Y. ^{[2
]}

机构：

[1] School of Electrical and Electronic Information, Xihua University, Chengdu

[2] State Grid Sichuan Electric Power Research Institute, Chengdu

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2020年 / 48卷 / 06期

基金：

中国国家自然科学基金;

关键词：

convolutional neural network; disturbance classification; feature extraction; power quality;

D O I：

10.19783/j.cnki.pspc.190550

中图分类号：

学科分类号：

摘要：

The existing power quality disturbance classification algorithms based on feature selection have poor robustness and weak anti-noise performance. This paper proposes an improved one-dimensional convolutional neural network model for power quality disturbance classification. Firstly, the eigenvectors of the power quality disturbance signal are extracted by three convolutional neural network sub-models, and then the extracted eigenvector is fused into a new one; finally, the classification is implemented by BP neural network. The result shows that the proposed algorithm has higher robustness, recognition rate and strong anti-noise abilities, because the new eigenvector has greater discrimination, compared with the old one-dimensional CNN models and the existing power quality disturbance classification algorithms. It provides a new idea for classifying power quality disturbance signals. © 2020 Power System Protection and Control Press. All rights reserved.

引用

页码：53 / 60

页数：7

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