基于卡尔曼滤波算法的风电机组塔顶位移监测方法

Tower Top Deflection Monitoring of aWind Turbine Using Kalman Filter Algorithm

针对风电机组塔顶位移在线监测问题,提出一种基于卡尔曼滤波算法的传感器信号融合方案。首先,建立用于塔顶转角测量的传感器迭代数学模型。然后,针对模型中传感器误差参数难以估计的问题,自行搭建实验平台优化模型参数,有效提高塔顶转角测量精度。在此基础上,通过仿真获取不同风速条件下塔顶位移与转角的线性关系,实现从塔顶转角到塔顶位移的转换。现场测试结果表明,基于该方法对塔顶位移监测的结果与仿真结果一致性高,且在额定风速区域机组塔顶位移达到最大,符合实际情况。该方案成本低廉,具备较高的工程应用价值,可为高耸结构姿态测量提供新的思路。

Aiming at the on-line monitoring of tower top deflection of a wind turbine,a sensor signal fusion scheme based on Kalman filter algorithm was proposed.Firstly,a sensor iterative mathematical model for tower top rotation angle measurement was established.Then,due to the difficulty of estimating the errors of the sensors,the parameters of the model were optimized by a self-built experimental platform,and the measurement accuracy of the tower top rotation angle was effectively raised.On this basis,the linear relation between the rotation angle and the deflection of the tower top was obtained by simulation,and the translation from the rotation angle to the deflection of the tower top was realized.The results of field tests indicate that the results of the tower top deflection monitoring by using this method are in a good agreement with those of simulation.The tower top deflection of the wind turbine reaches maximum when it operating at the rated wind speed,which is reasonable.The proposed scheme has high engineering application value for its low cost.It provides new ideas for high-rise structure attitude measurement.