  The use of state estimation technique offers a means of inferring the
rotor-effective wind speed based upon solely standard measurements of the
turbine. For the ease of design and computational concerns, such estimators are
typically built based upon simplified turbine models that characterise the
turbine with rigid blades. Large model mismatch, particularly in the power
coefficient, could lead to degradation in estimation performance. Therefore, in
order to effectively reduce the adverse impact of parameter uncertainties in
the estimator model, this paper develops a wind sped estimator based on the
concept of interacting multiple-model adaptive estimation. The proposed
estimator is composed of a bank of extended Kalman filters and each filter
model is developed based on different power coefficient mapping to match the
operating turbine parameter. Subsequently, the algorithm combines the wind
speed estimates provided by each filter based on their statistical properties.
In addition, the proposed estimator not only can infer the rotor-effective wind
speed, but also the uncertain system parameters, namely, the power coefficient.
Simulation results demonstrate the proposed estimator achieved better
improvement in estimating the rotor-effective wind speed and power coefficient
compared to the standard Kalman filter approach.
