微型电脑应用2025，Vol.41Issue(3)：72-76,5.
基于FFT-GAF和CNN的输电线路雷击过电压识别研究
Research on Recognition of Lightning Overvoltage in Transmission Lines Based on FFT-GAF and CNN
摘要
Abstract
Rapid and accurate identification of transmission line lightning faults is conducive to reducing the duration of faults and economic losses.Therefore,this paper proposes a lightning faults recognition method for transmission lines based on fast Fourier transform-Gramian angular field(FFT-GAF)and convolutional neural network(CNN).The lightning overvoltage data of the lines are subjected to FFT to obtain the overvoltage frequency domain characteristic data.The GAF is used to process the frequency domain feature datas,and the feature images of different overvoltage types are obtained.A CNN combined with transfer learning is proposed to classify the feature images to realize the lightning overvoltage recognition of transmission lines.The lightning fault waveform is obtained by PSCAD software for verification.The experimental results show that the recogni-tion accuracy of the proposed method is as high as 98.16％,which is superior to other models.关键词
卷积神经网络/格拉姆角场/快速傅里叶变换/迁移学习Key words
convolutional neural network/gramian angular field/fast Fourier transform/transfer learning分类
信息技术与安全科学引用本文复制引用
卓坚熊,席荣军,陈正雍,陈俊彬,刘友强..基于FFT-GAF和CNN的输电线路雷击过电压识别研究[J].微型电脑应用,2025,41(3):72-76,5.基金项目
中国南方电网有限责任公司科技项目(GDKJXM20220822) （GDKJXM20220822）
