高电压技术2024，Vol.50Issue(11)：5184-5191,8.DOI:10.13336/j.1003-6520.hve.20231990
基于多通道卷积神经网络的甚低频/低频雷电辐射电场波形分类方法
Classification Method for VLF/LF Lightning Radiated Electric Field Waveforms Based on Convolutional Neural Networks
摘要
Abstract
The lightning process generates multiple types of lightning electric field waveforms.Traditional classification methods based on waveform parameters are prone to make misclassification.To address this issue,we proposed a method of VLF/LF lightning electric field signal classification based on a multi-channel convolutional neural network.This method uses a deep network to directly process the field waveforms,reducing dependency on prior knowledge.The net-work was constructed with multiple convolutional kernels to effectively extract the multi-scale waveform features.The shortcut connections were introduced to accelerate model convergence.Based on the data collected in Hefei,a training dataset of four typical waveforms,namely,return stroke,preliminary breakdown,narrow bipolar event,and intracloud,was established.The training results show that the model achieves an accuracy of 99.4%.Compared with classic machine learning methods and deep learning models,the proposed model performs better in classification accuracy and training convergence speed.By using the knowledge distillation method,a model suitable for low-computing-power platforms can be obtained.The distilled model takes only 59 ms for single classification,with a 66%reduction in computing power re-quirements and a classification accuracy of 99.0%,demonstrating reliable application of the proposed model on low-computing-power platforms.关键词
卷积神经网络/VLF/雷电辐射电场/波形分类/模型部署Key words
convolutional neural network/VLF/lightning radiation electric field/waveform classification/model de-ployment引用本文复制引用
肖力郎,陈维江,王宇,贺恒鑫,傅中,向念文..基于多通道卷积神经网络的甚低频/低频雷电辐射电场波形分类方法[J].高电压技术,2024,50(11):5184-5191,8.基金项目
国家电网有限公司科技项目(5500-202120583A-0-5-SF).Project supported by Science and Technology Project of SGCC(5500-202120583A-0-5-SF). （5500-202120583A-0-5-SF）
