The main goal of this paper is to establish the present state of the art for wind farm control. The control area that will be focused on is the mechanical/aerodynamic part, which includes the wind turbines, their power production, fatigue and wakes affecting neighbouring wind turbines. The sub‐objectives in this area of research are as follows: (i) maximizing the total wind farm power production; (ii) following a reference for the total wind farm active power; and (iii) doing this in a manner that minimizes fatigue loading for the wind turbines in the farm. Each of these sub‐objectives is discussed, including the following important control issues: choice of input and output, control method and modelling used for controller design and simulation. The available literature from industry is also considered. Finally, a conclusion is presented discussing the established results, open challenges and necessary research. Copyright © 2014 John Wiley & Sons, Ltd.

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The theory and results of two experimental methods for estimating the modal damping of a wind turbine during operation are presented. Estimations of the aeroelastic damping of the operational turbine modes (including the effects of the aerodynamic forces) give a quantitative view of the stability characteristics of the turbine. In the first method the estimation of modal damping is based on the assumption that a turbine mode can be excited by a harmonic force at its natural frequency, whereby the decaying response after the end of excitation gives an estimate of the damping. Simulations and experiments show that turbine vibrations related to the first two tower bending modes can be excited by blade pitch and generator torque variations. However, the excited turbine vibrations are not pure modal vibrations and the estimated damping is therefore not the actual modal damping. The second method is based on stochastic subspace identification, where a linear model of the turbine is estimated alone from measured response signals by assuming that the ambient excitation from turbulence is random in time and space. Although the assumption is not satisfied, this operational modal analysis method can handle the deterministic excitation, and the modal frequencies and damping of the first tower and first edgewise whirling modes are extracted. Copyright © 2006 John Wiley & Sons, Ltd.

In wind farms, individual turbines disturb the wind field by generating wakes that influence other turbines in the farm. From a control point of view, there is an interest in dynamic optimization of the balance between fatigue and production, and an understanding of the relationship between turbines manifested through the wind field is hence required. This paper develops models for this relationship. The result is based on two new contributions: the first is related to the estimation of effective wind speeds, which serves as a basis for the second contribution to wind speed prediction models. Based on standard turbine measurements such as rotor speed and power produced, an effective wind speed, which represents the wind field averaged over the rotor disc, is derived. The effective wind speed estimator is based on a continuous–discrete extended Kalman filter that takes advantage of nonlinear time varying turbulence models. The estimator includes a nonlinear time varying wind speed model, which compared with literature results in an adaptive filter. Given the estimated effective wind speed, it is possible to establish wind speed prediction models by system identification. As the prediction models are based on the result related to effective wind speed, it is possible to predict wind speeds at neighboring turbines, with a separation of over 700 m, up to 1 min ahead reducing the error by 30% compared with a persistence method. The methodological results are demonstrated on data from an off‐shore wind farm. Copyright © 2011 John Wiley & Sons, Ltd.

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