基于EKF和UKF的风电机组非线性状态预测

Nonlinear state estimation of wind turbine based on EKF and UKF

风电机组的状态监测和故障诊断依赖于对其关键状态参数的参考值进行有效预测。文章采用基于系统模型的非线性状态评估方法对风电机组关键的状态参数进行预测。通过对风电机组进行建模仿真,构建扩展卡尔曼滤波和无迹卡尔曼滤波模型预测风电机组主轴转速和发电机电磁转矩,对比两种方法的预测结果在不同测量步长下的差异。结果显示,无迹卡尔曼滤波方法比扩展卡尔曼滤波方法具有更强的收敛性和稳定性,受测量步长影响更小。

Wind turbine condition monitoring and fault diagnosis are realizable based on prediction of key parameters of the wind turbine.The model-based nonlinear state estimation method is utilized to predict key parameters of the wind turbine in the paper.A wind turbine model is established for simulation.An extended Kalman filter （EKF） and an unscented Kalman filter （UKF） are presented to estimate the rotor speed and generator torque of a wind turbine.The comparison results of the prediction in different measurement step show that the UKF method has better convergence and stability and is less affected by the measurement step,compared with the EKF method.