Friction estimation in wind turbine blade bearings

The influence of friction on wind turbines (WTs) is often seen as undesirable as it has been proven to be the root cause of different severe damages, where the most vulnerable WT's components are bearings. Accurate modelling and identification of friction affects are thus required in order to characterise frictional behaviour and prevent severe damages. This paper addresses the problem on the real-time estimation of friction levels that vary in time with different rates in the WT's blade bearings. Because of ageing or damage progression in bearings, friction parameters change in time. Knowing the material properties and a damage model, monitoring of the friction can be used for condition monitoring of WT's blade bearings. The considered nonlinear identification methods suitable for estimation of fast varying parameters are the extended Kalman filter (EKF), the unscented Kalman filter, the particle filter (PF) and the PF combined with the EKF. This paper also proposes a novel modification of a differential evolution algorithm for the identification of parameters that slowly vary in time. The main contribution of this paper is the algorithm, which combines the modified differential evolution algorithm with optimal nonlinear filters for the estimation of parameters which vary at different rates. A LuGre friction model and a realistic WT sensor setup are selected. The techniques are benchmarked against simulated WT data, and their performances are compared and discussed. Copyright (C) 2015 John Wiley & Sons, Ltd.