Classification Method for VLF/LF Lightning Radiated Electric Field Waveforms Based on Convolutional Neural Networks
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.
convolutional neural networkVLFlightning radiation electric fieldwaveform classificationmodel de-ployment
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