Further improvements in the cost-effectiveness of wind turbines drives designers towards larger, lighter, more flexiblestructures in which more intelligent control systems play an important part in actively reducing the applied structural loads, avoiding the need for wind turbines to simply withstand the full force of the applied loads through the use of stronger, heavier and therefore more expensive structures. Controller research within the UPWIND project has been aimed at further developing such control strategies and ensuring that new, often larger and innovative turbines can be designed to use these techniques from the start. For this to be possible, it is important to build up full confidence in the effectiveness and the reliability of these strategies in all situations. To this end, the work reported in this paper covers several different aspects: full-scalefield testing to build confidence in the effectiveness of advanced control strategies; further development of advanced control strategies to prevent unwelcome side effects in any of the load cases that have to be considered during the design; the possibility of blades employing dual-pitch control; development of load estimation techniques that can reduce reliance on additional sensors that would otherwise be required; investigating the potential of light detection and ranging assisted feed-forward pitch control to mitigate extreme and fatigue loads; using system identification methods to improve controller tuning. The detailed results of the work presented in this paper are available in the published reports of the Control Systems work package of the UPWIND project. These reports also cover other results of the work package, which are not reported here, such as control during network faults such as voltage dips, voltage control at the point of connection to the network and gradual cut-out of wind turbines to improve output predictability in high winds. A summary report is also available
Wind farm control is an active and growing field of research in which the control actions of individual turbines in a farm are coordinated, accounting for inter-turbine aerodynamic interaction, to improve the overall performance of the wind farm and to reduce costs. The primary objectives of wind farm control include increasing power production, reducing turbine loads, and providing electricity grid support services. Additional objectives include improving reliability or reducing external impacts to the environment and communities. In 2019, a European research project (FarmConners) was started with the main goal of providing an overview of the state-of-the-art in wind farm control, identifying consensus of research findings, data sets, and best practices, providing a summary of the main research challenges, and establishing a roadmap on how to address these challenges. Complementary to the FarmConners project, an IEA Wind Topical Expert Meeting (TEM) and two rounds of surveys among experts were performed. From these events we can clearly identify an interest in more public validation campaigns. Additionally, a deeper understanding of the mechanical loads and the uncertainties concerning the effectiveness of wind farm control are considered two major research gaps.
In order to get reliable linear models for tuning controllers in real operation conditions, a procedure for identification in closed‐loop operation of wind turbine (WT) models at any time and placement is presented in this paper. This procedure has been tested using data from a nonlinear aeroelastic code, generated in a context reproducing real operation conditions of WTs, with the presence of three‐dimensional turbulence. The algorithms for model identification of WTs operating in closed loop together with the corresponding validation techniques are presented. To illustrate the procedure, data from one Bladed® WT model is used. Identification and validation of the model are presented and the resulting model is analysed and its characteristics are plotted. In the final sections of the paper, the model obtained from identification in closed loop and the model obtained by linearization are comparatively tested in terms of the performances obtained with the corresponding model‐based designed controllers applied to the Bladed® WT model. A description of future work concludes the paper. Copyright © 2009 John Wiley & Sons, Ltd.
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