Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

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基于小波系数PCA和SaDE-ELM的电能质量扰动信号分类

来源:电工电气发布时间:2021-04-30 15:30 浏览次数:9

基于小波系数PCA和SaDE-ELM的电能质量扰动信号分类

薛正爱1,黄陈蓉2,张建德2,支昊1,顾飞1
(1 南京工程学院 电气工程学院,江苏 南京 211167;
2 南京工程学院 计算机工程学院,江苏 南京 211167)
 
    摘 要:电能质量扰动信号分类是电能质量综合治理的前提,为提高分类精度,提出一种基于主成分分析(PCA) 和自适应差分进化(SaDE) 优化的极限学习机(ELM) 的电能质量扰动信号分类方法。对 8 种扰动信号用 db4 小波进行 10 层多分辨分解,与标准能量信号的能量差系数作为特征向量,PCA 对其降维处理,去除冗余特征,得到 4 维数据作为分类的样本数据集,利用 SaDE 算法对 ELM 的输入权值和隐含层节点偏置优化。通过仿真实验表明,提出的 SaDE-ELM 识别准确率更高,抗噪性更强,更适应于电能质量扰动分类。
    关键词:电能质量;多分辨分解;主成分分析;自适应差分进化;极限学习机
    中图分类号:TM711     文献标识码:A     文章编号:1007-3175(2021)04-0006-05
 
Power Quality Disturbance Signal Classification Based on PCA and SaDE-ELM
 
XUE Zheng-ai1, HUANG Chen-rong2, ZHANG Jian-de2, ZHI Hao1, GU Fei1
(1 School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China;
2 School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, China)
 
    Abstract: Power quality disturbance signal classification is the premise of comprehensive power quality control. In order to improve the classification accuracy, this paper proposes a method of power quality disturbance signal classification based on principal component analysis(PCA) and adaptive differential evolution (SaDE) optimization of extreme learning machine (ELM). The 8 kinds of disturbance signals are decomposed by db4 wavelet with 10 layers of multi-resolution, and the energy difference coefficient with the standard energy signal is used as the feature vector, and PCA is used to reduce the dimensionality, redundant features are removed, and 4-dimensional data is obtained as a sample data set for classification. The SaDE algorithm is used to optimize the input weights and hidden layer node bias of ELM. Simulation experiment, the proposed SaDE-ELM has higher recognition accuracy, stronger noise resistance and it is more suitable for power quality disturbance classification.
    Key words: power quality; multiresolution decomposition; principal component analysis; adaptive differential evolution; extreme learning machine
 
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