摘 要：基于风电机组载荷传递机制推导塔架载荷来源,利用Bladed软件验证塔架和机舱载荷计算公式正确性,发现风轮载荷和机舱加速度是塔架俯仰力矩与侧倾力矩载荷主要成分来源,结合Person系数评价机舱位移、倾角、发电机功率等风轮载荷表征量与塔架力矩载荷之间的高度相关性。以塔底载荷为研究对象,基于多元线性回归算法,构建以风电机组易测动力学参量为输入的塔底载荷代理模型,实时识别得到塔底合力矩载荷,通过大型风电机组仿真载荷数据测试代理模型的有效性,结果表明塔底极限载荷、等效疲劳载荷识别结果与仿真结果相对误差控制在6%以内,在此基础上提出一种风电机组塔架载荷在线识别与管理系统。
Abstract
The tower load source is derived based on the load tranfer mechanicsm of wind turbine, This paper derives the sour tower load based on the load transfer mechanism of wind turbine, and the calculation formulas for tower and nacelle loads are validated using Bladed software. It is found that the rotor loads and nacelle acceleration are the main components of the tower tilt and roll moments. The high correlation between the tower moment loads and rotor loads characteristics including nacelle displacement or tilt angle and generator power, is evaluated with Person coefficient. With the tower bottom load as research subject, the surrogate model for tower bottom load with easy-measuring parameters as input is constructed based on the multiple linear regression algorithm, and the tower bottom resultant moment load can be identified in real time. The effectiveness of model is tested by the simulated loads of large-scale wind turbine, and it is showed that the relative error of ultimate load and equivalent fatigue load between identified and simulated results is controlled within 6%. On this basis, an on-line system for tower load identification and management is proposed.
关键词
风电机组 /
塔架 /
动载荷 /
在线识别 /
线性回归 /
代理模型
Key words
wind turbines /
towers /
dynamic loads /
on-line load identification /
linear regression /
surrogate model
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基金
国家重点研发计划（2022YFB4201200）
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