振动频率法在风电机组混凝土塔筒索力监测中的研究进展
Research Progress of Vibration Frequency Method in Cable Force Monitoring of Hybrid Wind Towers for Wind Turbines
摘要: 振动频率法是当前风电混塔体外索力检测中综合性能最优的非损伤检测方法之一，但其从桥梁斜拉索向混塔体外索迁移面临若干关键技术障碍。风电混塔体外索无约束套管、单根离散张拉及转向装置的存在，使得经典频率–索力关系在该场景中面临离散钢绞线间耦合不足、有效长度不确定、边界条件复杂及抗弯刚度难以量化等多重挑战。本文系统梳理了振动频率法的理论基础及其在风电混塔中的适应性修正，结合实际案例分析了离散钢绞线耦合机制、转向装置等效边界处理、有效索长识别及抗弯刚度修正等关键影响因素的研究进展，介绍了基于深度学习与物理信息神经网络的索力智能化监测新方法，并对未来研究方向进行了展望。
Abstract: The vibration frequency method ranks among the most optimal non-destructive testing techniques with comprehensive performance for external cable force measurement of hybrid wind turbine towers at present. Nevertheless, several critical technical bottlenecks remain during the method’s migration from stay cables of bridge structures to external tendons of hybrid wind towers. Characterized by unconstrained protective casings, discrete single-strand tensioning and steering fittings, external tendons for hybrid wind towers bring multiple obstacles to the classical frequency- cable force correlation formula, including insufficient discrete coupling effect, indeterminate effective tendon length, complicated boundary constraints and difficulty in quantifying flexural rigidity. This paper systematically reviews the theoretical fundamentals of the vibration frequency method as well as its adaptive modification strategies for hybrid wind tower applications. Combined with practical engineering cases, state-of-the-art research concerning dominant influential factors is elaborated, covering the coupling mechanism of discrete steel strands, equivalent boundary simplification for steering devices, identification of effective tendon length and flexural rigidity correction. Novel intelligent cable force monitoring approaches based on deep learning and physics-informed neural networks (PINNs) are summarized, and prospective research trends are outlined for future investigations.
文章引用：屈德斌, 朱文凡, 赵万东, 武茂雄, 薛鹏鸣, 谢凯学. 振动频率法在风电机组混凝土塔筒索力监测中的研究进展[J]. 土木工程, 2026, 15(9): 77-89. https://doi.org/10.12677/hjce.2026.159225

参考文献

[1] 朱少辉. 风力发电机组钢混结构塔架研究[J]. 重型机械, 2022(5): 74-78.
[2] 王丹, 徐军, 贺广零, 等. 风电机组混凝土-钢混合塔筒技术现状与发展趋势[J]. 土木与环境工程学报(中英文), 2025, 47(6): 151-167.
[3] 曹胜平, 林恒峰, 张廷发. 陆上风电机组钢混塔架智能监测技术研究与应用[J]. 节能, 2026, 45(2): 88-91.
[4] 李艳, 丁通, 季长征, 等. 浅谈体外预应力加固技术预应力损失的计算方法[J]. 建设机械技术与管理, 2023, 36(4): 105-107.
[5] 喻文超, 罗国甘, 黄声富, 等. 基于光纤传感的混塔结构风机安全监测系统的研究与应用[J]. 中国设备工程, 2024(S1): 60-67.
[6] 朱东, 吕伟荣, 徐伟. 频率法在风电机组混塔预应力索索力检测中的可行性分析[J]. 四川水泥, 2024(12): 43-45.
[7] 张振利, 贾克勤, 刘庆阳, 等. 基于频率法的风电机组混塔索力识别影响因素分析[J]. 风能, 2024(9): 68-72.
[8] 张后禅. 风电混塔行业技术发展现状及标准体系分析[J]. 能源, 2023(7): 61-67.
[9] 贺冬冬, 唐现梓. 振动频率法在风电混塔索力检测中的应用[J]. 金属制品, 2025, 51(4): 41-44.
[10] Weaver, J.W., Timoshenko, S.P. and Young, D.H. (1991) Vibration Problems in Engineering. John Wiley & Sons.
[11] Irvine, H.M. (1981) Cable Structures. The MIT Press.
[12] Zui, H., Shinke, T. and Namita, Y. (1996) Practical Formulas for Estimation of Cable Tension by Vibration Method. Journal of Structural Engineering, 122, 651-656.
https://doi.org/10.1061/(asce)0733-9445(1996)122:6(651)
[13] 任伟新, 陈刚. 由基频计算拉索拉力的实用公式[J]. 土木工程学报, 2005(11): 26-31.
[14] 韩州斌, 李东升, 杨洋. 等值张拉时基于振动法的平行钢绞线索索力测试分析[J]. 公路交通科技, 2018, 35(2): 55-61.
[15] 张卓杰, 谷利雄, 王东强, 等. 刚性耦合对多索股结构自振特性的影响[J]. 铁道学报, 2021, 43(7): 153-160.
[16] 何雄君, 杨永超, 肖祥, 等. 基于有限元等效索长的振动频率法[J]. 桥梁建设, 2016, 46(6): 40-44.
[17] 姚帅, 戴巨川, 吕伟荣. 基于频率法的风电机组混合塔筒拉索索力检测技术研究[J]. 机械工程学报, 2026, 62(2): 385-396.
[18] 李伟, 郭跃, 赵万. 考虑抗弯刚度与垂度效应的频率法拉索索力测定方法研究[J]. 四川水泥, 2026(3): 135-138.
[19] 刘红波, 王龙轩, 郭刘潞, 等. 基于深度学习的建筑用短粗拉索索力智能识别[J]. 建筑结构学报, 2023, 44(9): 204-213.
[20] Wu, W., Chen, C., Lin, S. and Lai, G. (2023) Fully Automatic Cable Tension Monitoring Based on Vibration Measurements with Deep Learning Techniques. In: Limongelli, M.P., Giordano, P.F., Quqa, S., Gentile, C. and Cigada, A. Eds., Lecture Notes in Civil Engineering, Springer, 339-348.
https://doi.org/10.1007/978-3-031-39109-5_35
[21] Jeong, S., Kim, H., Lee, J. and Sim, S. (2020) Automated Wireless Monitoring System for Cable Tension Forces Using Deep Learning. Structural Health Monitoring, 20, 1805-1821.
https://doi.org/10.1177/1475921720935837
[22] 张凡, 张峰武, 孟立, 等. 基于小波变换与微调GoogleNet的风电机组混凝土塔筒预应力索碰摩智能识别方法[J]. 新能源科技, 2026, 7(2): 50-54.
[23] 郭子臣, 郑家榕, 包丽莉, 等. 基于LSTM的钢混风塔索力预测方法[J]. 建筑结构, 2025, 55(15): 119-123.
[24] Raissi, M., Perdikaris, P. and Karniadakis, G.E. (2019) Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations. Journal of Computational Physics, 378, 686-707.
https://doi.org/10.1016/j.jcp.2018.10.045
[25] 徐皓, 单德山, 吴康, 等. 基于物理信息神经网络的斜拉索索力识别[J/OL]. 土木与环境工程学报(中英文): 1-10.
https://link.cnki.net/urlid/50.1218.TU.20241216.0912.002, 2026-06-29.
[26] 张卓杰, 韩义泽, 徐建新, 等. 基于改进物理信息神经网络的耦合索股系统索力识别研究[J]. 振动与冲击, 2026, 45(5): 27-39.
[27] Zhang, W., Suo, W., Song, J. and Cao, W. (2026) Physics-Informed Neural Networks (PINNs) as Intelligent Computing Technique for Solving Partial Differential Equations: Limitation and Future Prospects. Science China Physics, Mechanics & Astronomy, 69, Article 214602.
https://doi.org/10.1007/s11433-024-2665-5

为你推荐