| 引用本文: | 王维博,张斌,曾文入,董蕊莹,郑永康.基于特征融合一维卷积神经网络的电能质量扰动分类[J].电力系统保护与控制,2020,48(6):53-60.[点击复制] |
| WANG Weibo,ZHANG Bin,ZENG Wenru,DONG Ruiying,ZHENG Yongkang.Power quality disturbance classification of one-dimensional convolutional neural networks based on feature fusion[J].Power System Protection and Control,2020,48(6):53-60[点击复制] |
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| 摘要: |
| 现有基于特征选取的电能质量扰动分类算法存在鲁棒性差、抗噪性能不强等问题。提出了一种改进的一维卷积神经网络用于电能质量扰动信号的分类。首先通过三个卷积神经网络子模型分别提取电能质量扰动信号的特征向量，然后将提取的特征向量融合为一个新的特征向量，最后通过BP神经网络实现分类。与改进前的一维卷积神经网络模型以及现有的电能质量扰动分类算法相比，该算法提取的特征向量具有更大的区分度。仿真结果表明，该算法有更好的鲁棒性和识别率，且抗噪能力强，为电能质量扰动信号分类提供了一种新思路。 |
| 关键词: 电能质量 卷积神经网络 扰动分类 特征提取 |
| DOI：10.19783/j.cnki.pspc.190550 |
| 投稿时间：2019-05-16修订日期：2019-11-11 |
| 基金项目:四川省高校重点实验室开放基金项目(szjj2017- 046);西华大学大健康管理促进中心(DJKG2019-005);国家自然科学基金(61571371);广东省自然科学基金(2015A 030313853) |
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| Power quality disturbance classification of one-dimensional convolutional neural networks based on feature fusion |
| WANG Weibo,ZHANG Bin,ZENG Wenru,DONG Ruiying,ZHENG Yongkang |
| (School of Electrical and Electronic Information, Xihua University, Chengdu 610039, China;State Grid Sichuan Electric Power Research Institute, Chengdu 610072, China) |
| Abstract: |
| 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. This work is supported by Sichuan University Key Laboratory Open Fund (No. szjj2017-046), Health Management Center of Xihua University (No. DJKG2019-005), National Natural Science Foundation of China (No. 61571371), and Natural Science Foundation of Guangdong Province (No. 2015A030313853). |
| Key words: power quality convolutional neural network disturbance classification feature extraction |
