大型风电机组轮毂处的有效风速难以直接测量,传统的风速估计方法具有滞后性,而泰勒冻结湍流假设忽略了激光雷达测量点到轮毂处的风场结构变化,影响了测量数据的准确性。针对上述问题,利用自回归移动平均与外源输入(Armax)模型对风演变过程进行建模。采用粒子群优化算法来估计模型参数,并对常规粒子群算法的惯性权重进行改进以防止陷入局部最小值。为确保风电系统控制动作的实时性、快速性,根据所建立的模型对轮毂处有效风速提前一步预测。运用Fast和Matlab/Simulink软件,以平均风速为7 m/s、湍流强度为A类级别的风况为例进行联合仿真,仿真结果表明,所提方法具有较高的实时性和准确性,比传统的风速估计方法效果更佳。

It is difficult to directly measure the effective wind speed at the hub of large wind turbines. Traditional wind speed estimation methods have hysteresis. The Taylor frozen turbulence assumption ignores the changes in the wind field structure from the lidar measurement point to the hub, which affects the accuracy of the measurement data. Aiming at the above problems, the auto-regressive moving average and external input (Armax) model were used to model the wind evolution process. The particle swarm optimization algorithm is used to estimate the model parameters, and the inertia weight of the conventional particle swarm algorithm is improved to avoid falling into a local minimum. In order to ensure the real-time and fast control action of the wind power system, the effective wind speed at the hub is predicted one step in advance according to the established model. Using Fast and Matlab/Simulink software, the joint simulation is carried out with an average wind speed of 7 m/s and a turbulence level A as an example. The simulation results show that the proposed method has higher real-time performance and accuracy and is more effective than traditional method of wind speed estimation.