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基于IHPO-BP神经网络的滑坡区塔线体系应力预测模型
Stress prediction model of tower-line system in landslide area based on IHPO-BP neural network
针对滑坡区输电塔线体系在边坡变形和风荷载作用下易发生杆件失效的问题,提出一种基于IHPO-BP神经网络的滑坡区塔线体系应力预测模型。首先利用改进的Circle混沌映射、平衡因子非线性策略以及Levy飞行对猎人猎物优化算法进行优化,进而利用改进的猎人猎物优化算法对BP神经网络的权值和阈值进行参数寻优,得到滑坡区塔线体系应力预测模型,将风向角、风速和塔腿支座位移作为模型输入,塔线体系杆件最大应力作为输出。预测结果表明:文中提出的IHPO-BP模型具有较高的收敛速度与预测精度,与HPO-BP模型相比,平均绝对误差下降了71.7%,均方根误差下降了76.6%,平均相对误差下降了71.8%。
In order to solve the problem that the transmission tower-line system in landslide area is prone to rod failure under the action of slope deformation and wind load, a stress prediction model of tower-line system in landslide area based on IHPO-BP neural network is proposed. Firstly, the improved Circle chaotic mapping, nonlinear strategy of balance factor and Levy flight are used to optimize the hunter-prey optimization algorithm, and then the improved hunter-prey optimization algorithm is used to optimize the weights and thresholds of BP neural network, and the stress prediction model of tower-line system in landslide area is obtained. The wind direction angle, wind speed and displacement of tower leg support are used as model inputs, and the maximum stress of tower-line system members is used as output. The prediction results show that the IHPO-BP model proposed in this paper has high convergence speed and prediction accuracy. Compared with the HPO-BP model, the average absolute error decreases by 71.7%, the root mean square error decreases by 76.6% and the average relative error decreases by 71.8%.
塔线体系;猎人猎物优化算法;Circle混沌映射;平衡因子非线性策略;BP神经网络
tower-line system; hunter-prey optimization; Circle chaotic mapping; nonlinear strategy of balance factor; BP neural network
周冬阳,王彦海,刘晓亮,李梦源,邹梦健
Zhou Dongyang,Wang Yanhai,Liu Xiaoliang,Li Mengyuan,Zou Mengjian
1.三峡大学电气与新能源学院;2.国网兰州供电公司
1.Collage of Electrical Engineer &2.New Energy, China Three Gorges University;3.amp;4.State Grid Lanzhou Electric Power Supply Company
njqxxyxb/article/abstract/20230524003