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

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基于VGG16圖像特征提取和SVM的電能質(zhì)量擾動(dòng)分類

來源：電工電氣發(fā)布時(shí)間：2023-07-27 14:27 瀏覽次數(shù)：343

基于VGG16圖像特征提取和SVM的電能質(zhì)量擾動(dòng)分類

童占北1，鐘建偉1，李禎維2，吳建軍2，李家俊2
(1 湖北民族大學(xué) 智能科學(xué)與工程學(xué)院，湖北 恩施 445000；
2 國網(wǎng)湖北省電力有限公司恩施供電公司，湖北 恩施 445000)
 
    摘 要：針對(duì)傳統(tǒng)電能質(zhì)量擾動(dòng)分類方法需人工選取特征量，易受人為經(jīng)驗(yàn)干擾的問題，提出一種基于 VGG16 圖像特征提取和支持向量機(jī) (SVM) 結(jié)合的電能質(zhì)量擾動(dòng)分類方法。通過格拉姆角場(chǎng) (GAF) 將電能質(zhì)量擾動(dòng)信號(hào)映射到極坐標(biāo)系中，生成格拉姆矩陣，并轉(zhuǎn)換為二維擾動(dòng)圖像；利用 VGG16 網(wǎng)絡(luò)自動(dòng)提取圖像特征的特點(diǎn)，將擾動(dòng)圖像輸入 VGG16 網(wǎng)絡(luò)中進(jìn)行提?。粚⑻崛〉奶卣鲾?shù)據(jù)作為 SVM 分類器的輸入，并引入十折交叉驗(yàn)證對(duì)SVM 進(jìn)行訓(xùn)練，以提升分類器的性能，最后對(duì)擾動(dòng)信號(hào)進(jìn)行準(zhǔn)確分類。仿真結(jié)果表明，該方法對(duì)于電能質(zhì)量擾動(dòng)的分類具有較高的準(zhǔn)確率。
    關(guān)鍵詞： 電能質(zhì)量；擾動(dòng)分類；格拉姆角場(chǎng)；VGG16 網(wǎng)絡(luò)；支持向量機(jī)；十折交叉驗(yàn)證
    中圖分類號(hào)：TM712     文獻(xiàn)標(biāo)識(shí)碼：A     文章編號(hào)：1007-3175(2023)07-0007-07
 
Power Quality Disturbance Classification Based on
VGG16 Image Feature Extraction and SVM
 
TONG Zhan-bei1, ZHONG Jian-wei1, LI Zhen-wei2, WU Jian-jun2, LI Jia-jun2
（1 College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China;
2 State Grid Hubei Electric Power Co., Ltd. Enshi Power Supply Company, Enshi 445000, China）
 
    Abstract: Traditional power quality disturbance classification methods need to manually select feature quantities, which are susceptible to human experience interference. Hence, the paper proposes a power quality disturbance classification method based on the combination of VGG16 image feature extraction and Support Vector Machine (SVM). It first maps power quality disturbance signals to the polar coordinate system through Gramian Angular Field (GAF) to generate the Gramian matrix which is transformed into a two-dimensional disturbance image. Second, the characteristic of VGG16 network to automatically extract image features is used to input disturbed images into VGG16 network for extraction. Third, the extracted feature data is used as the input of SVM classifier, ten-fold cross-validation is introduced to train SVM to improve the performance of the classifier, and then disturbance signals are classified in an accurate way. The simulation results show that this method has higher accuracy of power quality disturbances classification.
    Key words: power quality; disturbance classification; Gramian angular field; VGG16 network; support vector machine; ten-fold cross-validation
 
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