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.

Exoskeleton robotics has ushered in a new era of modern neuromuscular rehabilitation engineering and assistive technology research. The technology promises to improve the upper-limb functionalities required for performing activities of daily living. The exoskeleton technology is evolving quickly but still needs interdisciplinary research to solve technical challenges, e.g., kinematic compatibility and development of effective human–robot interaction. In this paper, the recent development in upper-limb exoskeletons is reviewed. The key challenges involved in the development of assistive exoskeletons are highlighted by comparing available solutions. This paper provides a general classification, comparisons, and overview of the mechatronic designs of upper-limb exoskeletons. In addition, a brief overview of the control modalities for upper-limb exoskeletons is also presented in this paper. A discussion on the future directions of research is included.

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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In this paper a set-membership approach for fault detection of a benchmark wind turbine is proposed. The benchmark represents relevant fault scenarios in the control system, including sensor, actuator and system faults. In addition we also consider parameter uncertainties and uncertainties on the torque coefficient. High noise on the wind speed measurement, nonlinearities in the aerodynamic torque and uncertainties on the parameters make fault detection a challenging problem. We use an effective wind speed estimator to reduce the noise on the wind speed measurements. A set-membership approach is used generate a set that contains all states consistent with the past measurements and the given model of the wind turbine including uncertainties and noise. This set represents all possible states the system can be in if not faulty. If the current measurement is not consistent with this set, a fault is detected. For representation of these sets we use zonotopes and for modeling of uncertainties we use matrix zonotopes, which yields a computationally efficient algorithm. The method is applied to the wind turbine benchmark problem without and with uncertainties. The result demonstrates the effectiveness of the proposed method compared to other proposed methods applied to the same problem. An advantage of the proposed method is that there is no need for threshold design, and it does not produce positive false alarms. In the case where uncertainty on the torque lookup table is introduced, some faults are not detectable. Previous research has not addressed this uncertainty. The method proposed here requires equal or less detection time than previous results.