风能控制技术建模.pdf

RESEARCH ARTICLE Wind plant power optimization through yaw control using a parametric model for wake effectsa CFD simulation study P . M. O. Gebraad1, F . W. Teeuwisse1, J. W. van Wingerden1, P . A. Fleming2, S. D. Ruben3, J. R. Marden3and L. Y. Pao3 1 Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands 2 National Renewable Energy Laboratory, Golden, Colorado, USA 3 University of Colorado, Boulder, Colorado, USA ABSTRACT This article presents a wind plant control strategy that optimizes the yaw settings of wind turbines for improved energy production of the whole wind plant by taking into account wake effects. The optimization controller is based on a novel internal parametric model for wake effects called the FLOw Redirection and Induction in Steady-state (FLORIS) model. The FLORIS model predicts the steady-state wake locations and the effective fl ow velocities at each turbine, and the result- ing turbine electrical energy production levels, as a function of the axial induction and the yaw angle of the different rotors. The FLORIS model has a limited number of parameters that are estimated based on turbine electrical power production data. In high-fi delity computational fl uid dynamics simulations of a small wind plant, we demonstrate that the optimization control based on the FLORIS model increases the energy production of the wind plant, with a reduction of loads on the turbines as an additional effect. Copyright 2014 John Wiley wind turbine yaw control; wind turbine wakes; optimization Correspondence J. W. van Wingerden, Mekelweg 2, 2628CD Delft, The Netherlands. E-mail: J.W.vanWingerdenTUDelft.nl Received 21 February 2014; Revised 6 October 2014; Accepted 28 October 2014 1. INTRODUCTION Each wind turbine in a cluster of wind turbines (a wind power plant) can infl uence the performance of other turbines through the wake that forms downstream of its rotor. The wake is a fl ow structure that is characterized by a reduced wind speed because the turbine rotor extracts kinetic energy from the incoming fl ow and an increased turbulence because the turbine obstructs the fl ow. If another turbine is standing in the path of a wake at a location where the fl ow has not yet fully recovered to free stream conditions, the reduced wind speed results in a lower electrical energy production of that turbine. In addition, the increased turbulence and shear in the wake may induce an increase in dynamic loads on the downstream turbine. These wake interaction effects have been studied extensively; see the studies of Vermeer et al., Crespo et al. and Sanderse for reviews of the literature.13The topology and amount of the wake interaction depend on time-varying atmospheric conditions (e.g., wind direction, wind speed, turbulence, and atmospheric stability) and on the operating point of each turbine that can be adjusted by changing their control settings (generator torque, pitch angles of the blades,4or yaw angle).57 In current industrial practice, wind turbines in wind plants are still controlled to maximize their own individual perfor- mance, ignoring the effect that the turbines have on other turbines through their wakes.8Recently, the wake interaction effects have become a more signifi cant fi eld of study in the research on wind turbine control algorithms because wind plants have grown in size, and more knowledge has become available on the loss of effi ciency because of the wake interaction effect. The study of Barthelmie et al.,9for example, reports an average energy production loss of 12% in an offshore wind plant caused by the wake effects (this percentage is averaged over the wind directions). Copyright 2014 John Wiley 23see Figure 6 for this comparison. We can see that by dividing the wake in different zones, as described in Section 3.4, and introducing a rotation-induced wake position offset (Section 3.3), we are able to better match the wake velocity profi le. 4. WIND PLANT YAW OPTIMIZATION USING A GAME-THEORETIC APPROACH In this work, we use the GT approach of Marden et al.17to perform an optimization of the yaw settings of the turbines in a wind plant with the objective of maximizing the total wind plant power production. The GT approach performs the optimization by making random perturbations to the yaw settings and holds the settings as a baseline setting if they yield an improvement of the wind plant total power so as to iteratively fi nd the global maximum of the wind plant total power. Following the control scheme of Figure 1 (shown in more detail in Figure 3), the optimization is based on the turbine power predictions of the FLORIS model. The randomized search of the GT approach is performed using the FLORIS simplifi ed model, and once the optimized settings are found, they are applied on the wind plant. In this section, we explain the optimization algorithm in more detail. A specifi cation of the different parameters of the optimization algorithm for a particul。