电瓷避雷器Issue(4)：18-28,11.DOI:10.16188/j.isa.1003-8337.2024.04.003
基于深度学习的雷电活动预测方法及其输电线路防雷应用
Lightning Activity Prediction Method Based on Deep Learning and its Application in Transmission Line Lightning Protection
摘要
Abstract
In order to improve the active protection ability of transmission lines against lightning disaster,a lightning activity prediction method based on Deep Learning was proposed,the lead time is 72 h,and the time and spatial accuracy are 3 h and 5 km,respectively.Based on the unified spatio-temporal grid,the lightning data in the forecast area were normalized,and the meteorological parameters strongly correlated with lightning activity were extracted by Chi-square unity test.A deep neural network model for lightning occurrence probability prediction was established,and the hyper-parameters combination of the model was optimized by Bayesian algorithm.A Convolutional Neural Network model is estab-lished to predict the number of lightning falls and the intensity of lightning current.The calculation re-sults show that the probability of detection and false alarm rate of the prediction model are 69.10％and 71.18％,and the average score of the prediction model for the number of lightning falls and lightning current intensity is 39.03％and 37.94％.The accuracy of the prediction for the lightning trip of Ultra high voltage lines is 87.5％,and the mean distance error is 4.01km.This method can be used to carry out lightning fault active protection of transmission lines based on forecast information,which is of great significance to reduce lightning disaster loss and improve lightning protection level of transmission net-work.关键词
输电线路/雷电预报/中尺度气象模式/深度学习/卡方检验/贝叶斯优化Key words
transmission lines/lightning forecast/mesoscale meteorological models/deep learning/chi-square test/bayesian optimization引用本文复制引用
张永刚,谷山强,李健,吴大伟,王宇..基于深度学习的雷电活动预测方法及其输电线路防雷应用[J].电瓷避雷器,2024,(4):18-28,11.基金项目
国家自然科学基金项目(编号:52007037).Project supported by National Natural Science Foundation of China(No.52007037). （编号:52007037）
