Abstract-This paper studies the load reduction potential of a prototyped "smart" rotor. This is, a rotor where the blades are equipped with a number of control devices that locally change the lift profile on the blade, combined with appropriate sensors and controllers. Experimental models, using dedicated system identification techniques, are developed of a scaled rotating two-bladed "smart" rotor of which each blade is equipped with trailing-edge flaps and strain sensors. A feedback controller based on -loop shaping combined with a fixed-structure feedforward control are designed that minimizes the root bending moment in the flapping direction of the two blades. We evaluated the performance using a number of different realistic load scenarios. We show that with appropriate control techniques the variance of the load signals can be reduced up to 90%.

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SUMMARYThe trend with offshore wind turbines is to increase the rotor diameter as much as possible to decrease the costs per kWh. The increasing dimensions have led to the relative increase in the loads on the wind turbine structure. Because of the increasing rotor size and the spatial load variations along the blade, it is necessary to react to turbulence in a more detailed way: each blade separately and at several separate radial distances. This combined with the strong nonlinear behavior of wind turbines motivates the need for accurate linear parameter-varying (LPV) models for which advanced control synthesis techniques exist within the robust control framework. In this paper we present a closed-loop LPV identification algorithm that uses dedicated scheduling sequences to identify the rotational dynamics of a wind turbine. We assume that the system undergoes the same time variation several times, which makes it possible to use timeinvariant identification methods as the input and the output data are chosen from the same point in the variation of the system. We use time-invariant techniques to identify a number of extended observability matrices and state sequences that are inherent to subspace identification identified in a different state basis. We show that by formulating an intersection problem all states can be reconstructed in a general state basis from which the system matrices can be estimated. The novel algorithm is applied on a wind turbine model operating in closed loop.

For the cost per kilowatt hour to be decreased, the trend in offshore wind turbines is to increase the rotor diameter as much as possible. The increasing dimensions have led to a relative increase of the loads on the wind turbine structure; thus, it is necessary to react to disturbances in a more detailed way, e.g. each blade separately. The disturbances acting on an individual wind turbine blade are to a large extent deterministic; for instance, tower shadow, wind shear, yawed error and gravity are depending on the rotational speed and azimuth angle and will change slowly over time. This paper aims to contribute to the development of individually pitch‐controlled blades by proposing a lifted repetitive controller that can reject these periodic load disturbances for modern fixed‐speed wind turbines and modern variable‐speed wind turbines operating above‐rated. The performance of the repetitive control method is evaluated on the UPWIND 5 MW wind turbine model and compared with typical individual pitch control. Simulation results indicate that for relatively slow changing periodic wind disturbances, this lifted repetitive control method can significantly reduce the vibrations in the wind turbine structure with considerably less high‐frequent control action. Copyright © 2012 John Wiley & Sons, Ltd.

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