Abstract
Abstract. The current trend toward larger wind turbine rotors leads to high periodic loads across the components due to the non-uniformity of inflow across the rotor. To address this, we introduce a blade-mounted lidar on each blade to provide a preview of inflow wind speed that can be used as a feedforward control input for the mitigation of such periodic blade loads. We present a method to easily determine blade-mounted lidar parameters, such as focus distance, telescope position, and orientation on the blade. However, such a method is accompanied by uncertainties in the inflow wind speed measurement, which may also be due to the induction zone, wind evolution, “cyclops dilemma”, unidentified misalignment in the telescope orientation, and the blade segment orientation sensor. Identification of these uncertainties allows their inclusion in the feedback–feedforward controller development for load mitigation. We perform large-eddy simulations, in which we simulate the blade-mounted lidar including the dynamic behaviour and the induction zone of one reference wind turbine for one above-rated inflow wind speed. Our calculation approach provides a good trade-off between a fast and simple determination of the telescope parameters and an accurate inflow wind speed measurement. We identify and model the uncertainties, which can then be directly included in the feedback–feedforward controller design and analysis. The rotor induction effect increases the preview time, which needs to be considered in the controller development and implementation.
Highlights
The ongoing trend of steadily growing rotor diameters of wind turbines results in dynamic loads across the rotor swept area which are becoming more uneven
To assess the performance efficiency of the blade-mounted lidar-based inflow wind speed measurement, we introduce a new signal called the blade-effective wind speed, which is determined as the contribution of the inflow wind speed on each blade segment ui(r) to the flapwise blade root bending moment; the inflow wind speed refers to the longitudinal wind speed in the rotor axis direction
To determine the optimal preview time for a given focus distance, we evaluate the cross-correlation between the blade effective and the corrected inflow wind speeds, with k ∈ {col, yaw, tilt}, and we choose the index of the peak value as the available preview time
Summary
The ongoing trend of steadily growing rotor diameters of wind turbines results in dynamic loads across the rotor swept area which are becoming more uneven. Due to the so-called rotational sampling or eddy slicing effect, the blade samples the inhomogeneous wind field with frequencies determined by the rotor speed. The dynamic blade loads are concentrated at the multiples of the rotational frequency, i.e. 1P , 2P , 3P , . The control surfaces on the rotor are becoming more localized and in addition to individual (blade) pitch control, local active or passive blade load mitigation concepts (e.g. trailing edge flaps) have been researched for several years. In addition to the proven feedback control based on rotor speed and individual blade root bending moment measurements, feedforward control using either observer techniques or lidar-assisted preview information of the inflow has been investigated for collective and individual pitch as well as trailing edge flap control. There are methods that can be applied in the feedback– feedforward controller design to guarantee robust stability and performance in the presence of inherent uncertainties in the lidar measurement
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