The wind turbines within a wind farm impact each other's power production and loads through their wakes. Wake control strategies, aiming to reduce wake effects, receive increasing interest by both the research community and the industry. A number of recent simulation studies with high fidelity wake models indicate that wake mitigation control is a very promising concept for increasing the power production of a wind farm and/or reducing the fatigue loading on wind turbines' components. The purpose of this paper is to study the benefits of wake mitigation control in terms of lifetime power production and fatigue loading on several existing full-scale commercial wind farms with different scale, layouts, and turbine sizes. For modeling the wake interactions, Energy Research Centre of the Netherlands' FarmFlow software is used: a 3D parabolized Navier-Stokes code, including a kturbulence model. In addition, an optimization approach is proposed that maximizes the lifetime power production, thereby incorporating the fatigue loads into the optimization criterion in terms of a lifetime extension factor. KEYWORDS active wake control, fatigue loads reduction, power production maximization, wake mitigation, wind farm control INTRODUCTIONWind turbines are being densely clustered in wind farms and effect each other's power production and loading through their wakes. Because of the reduced wind velocity and increased turbulence, a turbine operating in the wake of another turbine will produce less power and experience increased fatigue loading. Still, the common practice is to let each wind turbine maximize its individual power capture, thereby disregarding its effect on other turbines. This "greedy" approach is not optimal with respect to the power production of the whole wind farm. At the beginning of this century, researchers started the development of a cooperative approach to operate wind farms 1-3 that aim at maximizing the power production of the whole farm, while at the same time, trying to reduce the fatigue loading on the wind turbines. These methods for mitigation of wake effects are called here active wake control (AWC).There are 2 major classes of AWC methods. The first concept aims at reducing the wake deficit downstream by reducing the axial induction factor of upstream wind turbines, known as axial induction control (also referred to as heat and flux or pitch-based AWC). This is achieved by operating the turbines at the windward side at an increased blade pitch angle. 4,5 In practice, increased pitch angle could indirectly be achieved by reducing the power production of the turbine (derating). While the power production of the derated machines decreases, the wind velocity in their wakes increases, allowing to produce more power at downstream wind turbines, and, possibly, raises the overall farm production. Initial experiments in a wind tunnel 5,6 and in the field 7 have shown that the power production of a wind farm may potentially increase under pitch-based AWC. However, more-recent high-fidelity simulations 8 a...
This paper examines the performance of centralized and decentralized feedback controllers on a plate with multiple colocated velocity sensors and force actuators. The performance is measured by the reduction in either kinetic energy or sound radiation, when the plate is excited with a randomly distributed, white pressure field or colored noise. The trade-off between performance and control effort is examined for each case. The controllers examined are decentralized absolute velocity feedback, centralized absolute velocity feedback control and linear quadratic Gaussian ͑LQG͒ control. It is seen that, despite the fact that LQG control is a centralized, dynamic controller, there is little overall performance improvement in comparison to decentralized direct velocity feedback control if both are limited to the same control effort.
With the growth of wind energy worldwide, an increased interest in wind farm control has become visible, with Active Power Control (APC) and Active Wake Control (AWC) being two primary examples. Both these methods rely on the down-regulation (i.e., operation using sub-optimal power settings) of wind turbines in order to provide such services. Apart from these services, down-regulation also affects the loads acting on a wind turbine. Hence, it is important to analyze the effects on the lifetime of wind turbine components, e.g., the tower, blades and rotor shaft. Earlier research on APC for wind farms has resulted in several down-regulation methods which were shown to reduce fatigue loads for some wind turbine components. One of these methods is called the percentage reserve method, which makes it possible for the wind turbine to generate a desired percentage of the available power at every wind speed. In this paper, different down-regulation strategies using the percentage reserve method are assessed on their capability of reducing fatigue loads. The performance of the different control strategies is compared using aeroelastic simulations and by comparing the Damage Equivalent Loads (DELs) of several components for the whole range of operational wind speeds. The fatigue lifetime is analyzed by combining the DELs with a wind speed distribution for the turbine specific wind class. The results show that all down-regulation strategies are capable of achieving significant lifetime fatigue load reductions for some wind turbine components. Whichever strategy provides the best performance, depends on the user's wishes as well as the environmental conditions and the wind turbine in question.
This paper considers the optimization of a velocity feedback controller with a collocated force actuator, to minimize the kinetic energy of a simply supported beam. If the beam is excited at a single location, the optimum feedback gain varies with the position of the control system. It is shown that this variation depends partly on the location of the control force relative to the exciting force. If a distributed excitation is assumed, that is random in both time and space, a unique optimum value of the feedback gain can be found for a given control location. The effect of the control location on performance and the optimal feedback gain can then be examined and is found to be limited provided the control locations are not close to the ends of the beam. The optimization can also be performed for a multichannel velocity feedback system. Both a centralized and a decentralized controller are considered. It is shown that the difference in performance between a centralized and a decentralized controller is small, unless the control locations are closely spaced. In this case the centralized controller effectively feeds back a moment proportional to angular velocity as well as a force proportional to a velocity. It is also shown that the optimal feedback gain can be approximated on the basis of a limited model and that similar results can be achieved.
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