基于IPOA-BP的输电塔复合基础极限抗拔承载力预测模型  

Prediction model of ultimate pullout capacity of transmission tower composite foundation based on IPOA-BP

作　　者：杨世强 李小来 王彦海[2] 曹铖 马立 尹恒伟 Yang Shiqiang;Li Xiaolai;Wang Yanhai;Cao Cheng;Ma Li;Yin Hengwei(EHV Company of State Grid Hubei Electric Power Co.,Ltd.,Wuhan 430051,China;College of Electrical Engineering&New Energy,China Three Gorges University,Yichang 443002,China)

机构地区：[1]国网湖北省电力有限公司超高压公司,武汉430051 [2]三峡大学电气与新能源学院,宜昌443002

出　　处：《国外电子测量技术》2024年第4期105-116,共12页Foreign Electronic Measurement Technology

基　　金：国家自然科学基金(U22A20600,52079070)项目资助。

摘　　要：为了实现输电塔复合基础极限抗拔承载力的准确预测,克服传统理论、经验公式误差大,计算慢的问题,提出一种改进鹈鹕智能算法(IPOA)来优化BP神经网络的承载力预测模型。首先,利用SPM混沌映射、Levy飞行以及融合非线性惯性权重因子ω的正余弦优化策略,对鹈鹕优化算法(POA)改进;然后,利用IPOA对BP神经网络的权值和阈值参数寻优,得到IPOA-BP预测模型;最后,基于验证后的数值试验构建数据集,对IPOA-BP预测模型进行训练和测试。结果表明,IPOA-BP与POA-BP预测模型相比,方根误差下降65.75%,绝对平均误差下降65.79%,平均相对误差下降65.60%,可见IPOA-BP神经网络能够实现复合基础抗拔承载力较准确的预测,为该类型基础的承载力预测提供了新方法。In order to achieve accurate prediction of the ultimate elevation bearing capacity of the composite foundation of transmission towers and overcome the problems of large error and slow calculation of the theoretical or traditional empirical formulas,an improved pelican intelligent algorithm(IPOA)is proposed to optimize the bearing capacity prediction model of the BP neural network.Firstly,the pelican optimization algorithm(POA)is optimized using SPM chaotic mapping,Levy flight,and a positive cosine optimization strategy that incorporates nonlinear inertial weight factorω.Then,the optimized IPOA is used to find the optimization of the weight and threshold parameters of the BP neural network,and the IPOA-BP prediction model is obtained;finally,a dataset is constructed based on validated simulation experiments and the IPOA-BP prediction model is trained and tested.The results show that compare with the POA-BP prediction model,the square root error of IPOA-BP decreases by 65.75%,the absolute average error decreases by 65.79%,and the average relative error decreases by 65.60%,it can be seen that IPOA-BP neural network can achieve a more accurate prediction of the composite foundation's resistance to elevation bearing capacity,which provides a new method for the prediction of the bearing capacity of this type of foundation.

关 键 词：改进鹈鹕优化算法 复合基础 BP神经网络 SPM混沌映射 正余弦优化策略 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TU391[自动化与计算机技术—控制科学与工程]