基于扩张变参数模型的风力机自适应故障估计

Adaptive fault estimation for wind turbines using augmented-state parameter-varying model

风力发电系统是复杂的空气动力学系统,有效的故障估计是保证发电系统可靠运行的重要方法.本文基于风力机变参数模型,提出一种基于扩张变参数模型的风力机自适应故障估计方法.首先阐述了风力机健康和故障变参数模型,基于此构造故障扩张模型,并利用线性变换得分块矩阵可观测标准型来完成自适应极点配置,进一步分别在扩张系统为奇、偶阶次下给出观测器设计定理及收敛性证明,从而实现扩张自适应观测器设计.最后,在4.8 MW的风力机标准模型上考虑系统元部件和执行器故障的在线估计.仿真结果验证了本文方法的有效性和可靠性.

As a wind turbine system is a complex aerodynamic system, an effective fault estimation technique is required for its reliable operation and power generation. In this paper, an adaptive fault estimation method is proposed for wind turbines based on parameter-varying model scheme. First, the healthy and faulty parameter-varying models are considered. Then, the augmented system is formulated and a block-matrix skill with observable canonical form is used to enable the pole placement for completing the adaptive observer design. Third, the theorems are presented, which state that the estimation errors of the observer are convergent under the odd and even orders of the system, respectively. Finally, the simulation experiments are carried out on the bench mark model of 4.8MW wind turbine, considering the online estimation of the component and actuator faults. The results show that the proposed method is effective and reliable.