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

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基于小波系數PCA和SaDE-ELM的電能質量擾動信號分類

來源:電工電氣發布時間:2021-04-30 15:30 瀏覽次數:13

基于小波系數PCA和SaDE-ELM的電能質量擾動信號分類

薛正愛1,黃陳蓉2,張建德2,支昊1,顧飛1
(1 南京工程學院 電氣工程學院,江蘇 南京 211167;
2 南京工程學院 計算機工程學院,江蘇 南京 211167)
 
    摘 要:電能質量擾動信號分類是電能質量綜合治理的前提,為提高分類精度,提出一種基于主成分分析(PCA) 和自適應差分進化(SaDE) 優化的極限學習機(ELM) 的電能質量擾動信號分類方法。對 8 種擾動信號用 db4 小波進行 10 層多分辨分解,與標準能量信號的能量差系數作為特征向量,PCA 對其降維處理,去除冗余特征,得到 4 維數據作為分類的樣本數據集,利用 SaDE 算法對 ELM 的輸入權值和隱含層節點偏置優化。通過仿真實驗表明,提出的 SaDE-ELM 識別準確率更高,抗噪性更強,更適應于電能質量擾動分類。
    關鍵詞:電能質量;多分辨分解;主成分分析;自適應差分進化;極限學習機
    中圖分類號:TM711     文獻標識碼:A     文章編號: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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