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Abstract-Large scale wind turbines are lightly damped mechanical structures driven by wind that is constantly fluctuating. In this paper, we address the design of a model-based receding horizon control scheme to reduce the structural loads in the transmission system and the tower, as well as provide constant (or at least smooth) power generation. Our controller incorporates two optimization problems: one to predict or estimate mean wind speed, given LIDAR data, and the other to carry out receding horizon control to choose the control inputs. The method is verified against an existing wind turbine control system, and shows reductions in both extreme loads and power fluctuations by 80% and 90% respectively, when compared to a conventional controller.

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In this article, a general method for model/controller order reduction of switched linear dynamical systems is presented. The proposed technique is based on the generalised gramian framework for model reduction. It is shown that different classical reduction methods can be developed into a generalised gramian framework. Balanced reduction within a specified frequency bound is developed within this framework. In order to avoid numerical instability and also to increase the numerical efficiency, generalised gramian-based Petrov-Galerkin projection is constructed instead of the similarity transform approach for reduction. The framework is developed for switched controller reduction. To the best of our knowledge, there is no other reported result on switched controller reduction in the literature. The method preserves the stability under an arbitrary switching signal for both model and controller reduction. Furthermore, it is applicable to both continuous and discrete time systems for different classical gramian-based reduction methods. The performance of the proposed method is illustrated by numerical examples.