| 基于卷积神经网络的高压电缆局部放电模式识别 |
| |
| 引用本文： | 杨帆,王干军,彭小圣,文劲宇,陈清江,杨光垚,李朝晖.基于卷积神经网络的高压电缆局部放电模式识别[J].电力自动化设备,2018,38(5). |
| |
| 作者姓名： | 杨帆 王干军 彭小圣 文劲宇 陈清江 杨光垚 李朝晖 |
| |
| 作者单位： | 华中科技大学电气与电子工程学院强电磁工程与新技术国家重点实验室;广东电网公司中山供电局 |
| |
| 基金项目： | 国家自然科学基金资助项目(51541705)；湖北省自然科学基金资助项目(2016CFB536)；中国南方电网公司科技项目资助(GDKJXM20172769) |
| |
| 摘 要： | 由高压电缆不同类型缺陷诱发的局部放电(PD)的识别难度较大,尤其是某些相似度较高的电缆绝缘缺陷类型难以区分。提出了一种基于卷积神经网络(CNN)的高压电缆PD模式识别方法,研究了不同网络层数、不同激活函数以及不同池化方式对识别效果的影响,并与传统的支持向量机(SVM)和反向传播神经网络(BPNN)算法进行了对比。结果表明,相比SVM和BPNN,CNN的总体识别精度分别提高了3.71%和4.06%,且能较好地识别具有高相似度的电缆缺陷类型。
|
| 关 键 词： | 高压电缆 局部放电 卷积神经网络 模式识别 深度学习 |
Partial discharge pattern recognition of high-voltage cables based on convolutional neural network |
| |
| YANG Fan,WANG Ganjun,PENG Xiaosheng,WEN Jinyu,CHEN Qingjiang,YANG Guangyao and LI Zhaohui.Partial discharge pattern recognition of high-voltage cables based on convolutional neural network[J].Electric Power Automation Equipment,2018,38(5). |
| |
| Authors: | YANG Fan WANG Ganjun PENG Xiaosheng WEN Jinyu CHEN Qingjiang YANG Guangyao and LI Zhaohui |
| |
| Affiliation: | State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China,Zhongshan Power Supply Bureau of Guangdong Power Grid Corporation, Zhongshan 528400, China,State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China,State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China,Zhongshan Power Supply Bureau of Guangdong Power Grid Corporation, Zhongshan 528400, China,State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China and State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
| |
| Abstract: | The recognition of PD(Partial Discharge) caused by different types of defects in high-voltage cables is difficult, especially the recognition of PD caused by cable insulation defects with high similarity. The CNN(Convolutional Neural Network)-based PD pattern recognition method for high-voltage cables is presented. The influences of different network layers, activation functions and pooling methods of CNN on recognition effect are studied. The proposed method is also compared with SVM(Support Vector Machine) and BPNN(Back Propagation Neural Network) method, and the results show that the overall recognition accuracy of CNN is respectively 3.71% and 4.06% higher than that of SVM and BPNN. Furthermore, it is proved that the cable insulation defects with high similarity can be effectively recognized by CNN. |
| |
| Keywords: | high-voltage cables partial discharge convolutional neural network pattern recognition deep learning |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《电力自动化设备》浏览原始摘要信息 |
|
点击此处可从《电力自动化设备》下载全文 |
|
