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针对风力机桨距的系统故障问题,提出一种基于变遗忘因子递推最小二乘算法(Variable Forgetting Factor Recursive Least-Squares,简称VFF-RLS)的故障诊断方法。根据桨距执行器故障会引起系统参数变化的特点,采用该算法对变化的参数进行估计,将执行器故障诊断问题转化为参数估计问题。桨距执行器模型经过离散转化为系统辨识模型,进而实现对时变的桨距执行器自然频率和阻尼系数进行辨识估计,且通过自动调整遗忘因子大小保证了辨识算法的收敛速度和辨识精度。仿真结果表明,所提出的方法能够有效诊断桨距执行器故障。
Aiming at the system fault of wind turbine pitch, a fault diagnosis method based on Variable Forgetting Factor Recursive Least-Squares (VFF-RLS) is proposed. According to the fact that the fault of the pitch actuator will cause the change of the system parameters, the algorithm is used to estimate the changing parameters, which transforms the actuator fault diagnosis into the parameter estimation. The pitch actuator model is discretely transformed into a system identification model, and then the natural frequency and damping coefficient of the time-varying pitch actuator are estimated and identified. The convergence speed and the identification accuracy of the identification algorithm are guaranteed by automatically adjusting the size of the forgetting factor. Simulation results show that the proposed method can effectively diagnose pitch actuator failure.