摘要
滑坡区输电铁塔基础发生位移时,会导致铁塔的最大位移及杆件所受最大应力发生变化,建立铁塔状态预测模型可得到铁塔的最大位移及杆件所受最大应力变化趋势,进而预防灾害事故的发生。提出一种改进麻雀搜索算法优化BP神经网络的预测模型,首先利用Sin混沌序列与步长因子动态调整策略对麻雀搜索算法进行优化,其次用优化后的模型对BP神经网络的权值及阈值进行参数寻优,得到预测模型。将铁塔基础在XYZ方向的位移值作为预测模型的输入量,得到铁塔最大位移值及铁塔杆件最大应力的预测值。本预测模型较BP神经网络模型相比,方根误差RSME值最高下降了63.4%,平均相对误差MAPE值最高下降了60.4%,绝对值平均绝对误差MAE值最高下降了62.6%,同时本文预测模型预测值符合真实值的变化趋势,综上本预测模型能较准确地预测输电铁塔运行状态,为其安全运行提供有力保障。
When the transmission tower foundation in landslide area is displaced,the maximum displacement of the tower and the maximum stress of the rod will change.The state prediction model of the tower can be established to obtain the maximum displacement of the tower and the maximum stress of the rod,so as to prevent the occurrence of disaster acidents.Proposes an improved sparrow search algorithm to optimize the prediction model of BP neural network.Firstly,Sin chaotic sequence and the dynamic adjustment strategy of step factor are used to optimize the sparrow search algorithm.Secondly,the optimized model is used to optimize the weights and thresholds of BP neural network to obtain the prediction model.The displacement value of the tower foundation in the direction XYZ is taken as the input of the prediction model,and the maximum displacement value of the tower and the predicted maximum stress value of the tower members are obtained.Compared with the model of BP neural network,the root error RSME value decreased by 63.4%,the average relative error MAPE value decreased by 60.4%,and the absolute mean absolute error MAE value decreased by 62.6%.At the same time,the predicted value of the prediction model in this paper was in line with the changing trend of the real value.In conclusion,the prediction model can accurately predict the operation state of the transmission tower and provide strong guarantee for its safe operation.
作者
李梦源
董瑞科
王彦海
周冬阳
邹梦健
Li Mengyuan;Dong Ruike;Wang Yanhai;Zhou Dongyang;Zou Mengjian(Hubei Provincial Engineering Technology Research Center for Power Transmission Line,China Three Gorges University,Yichang 443002,China;College of Electrical and New Energy,China Three Gorges University,Yichang 443002,China;Yunnan Institute of Water&.Hydropower Engineering Investigation,Design and Research,Kunming 650051,China)
出处
《电子测量技术》
北大核心
2023年第11期74-82,共9页
Electronic Measurement Technology
基金
国家自然科学基金(52079070)项目资助。
关键词
滑坡区输电铁塔
BP神经网络
麻雀搜索算法
步长因子动态调整
transmission tower in landslide area
BP neural network
sparrow search algorithm
step size factor dynamic adjustment
