Kalman-based interacting multiple-model wind speed estimator for wind turbines

TL;DR

This paper introduces a Kalman-based interacting multiple-model estimator for wind turbines that improves rotor-effective wind speed estimation by accounting for parameter uncertainties, outperforming standard Kalman filters.

Contribution

It develops a novel adaptive estimation method combining multiple extended Kalman filters to better handle turbine parameter uncertainties and improve wind speed estimation accuracy.

Findings

Enhanced wind speed estimation accuracy

Better parameter uncertainty handling

Outperforms standard Kalman filter in simulations

Abstract

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.