Wind farm control using dynamic concepts is a research topic that is receiving an increasing amount of interest. The main concept of this approach is that dynamic variations of the wind turbine control settings lead to higher wake turbulence, and subsequently faster wake recovery due to increased mixing. As a result, downstream turbines experience higher wind speeds, thus increasing their energy capture. In dynamic induction control (DIC), the magnitude of the thrust force of an upstream turbine is varied. Although very effective, this approach also leads to increased power and thrust variations, negatively impacting energy quality and fatigue loading. In this paper, a novel approach for the dynamic control of wind turbines in a wind farm is proposed: using individual pitch control, the fixed‐frame tilt and yaw moments on the turbine are varied, thus dynamically manipulating the wake. This strategy is named the helix approach because the resulting wake has a helical shape. Large eddy simulations of a two‐turbine wind farm show that this approach leads to enhanced wake mixing with minimal power and thrust variations.
With the trend of increasing wind turbine rotor diameters, the mitigation of blade fatigue loadings is of special interest to extend the turbine lifetime. Fatigue load reductions can be partly accomplished using individual pitch control (IPC) facilitated by the so‐called multiblade coordinate (MBC) transformation. This operation transforms and decouples the blade load signals in a yaw‐axis and tilt‐axis. However, in practical scenarios, the resulting transformed system still shows coupling between the axes, posing a need for more advanced multiple input multiple output (MIMO) control architectures. This paper presents a novel analysis and design framework for decoupling of the nonrotating axes by the inclusion of an azimuth offset in the reverse MBC transformation, enabling the application of simple single‐input single‐output (SISO) controllers. A thorough analysis is given by including the azimuth offset in a frequency‐domain representation. The result is evaluated on simplified blade models, as well as linearizations obtained from the NREL 5–MW reference wind turbine. A sensitivity and decoupling assessment justify the application of decentralized SISO control loops for IPC. Furthermore, closed‐loop high‐fidelity simulations show beneficial effects on pitch actuation and blade fatigue load reductions.
With the ever increasing power rates of wind turbines, more advanced control techniques are needed to facilitate tall towers that are low in weight and cost-effective but in effect more flexible. Such soft-soft tower configurations generally have their fundamental side-side frequency in the below-rated operational domain. Because the turbine rotor practically has or develops a mass imbalance over time, a periodic and rotor-speed dependent side-side excitation is present during below-rated operation. Persistent operation at the coinciding tower and rotational frequency degrades the expected structural life span. To reduce this effect, earlier work has shown the effectiveness of active tower damping control strategies using collective pitch control. A more passive approach is frequency skipping by inclusion of speed exclusion zones, which avoids prolonged operation near the critical frequency. However, neither of the methods incorporates a convenient way of performing a trade-off between energy maximization and fatigue load minimization. Therefore, this paper introduces a quasi-linear parameter varying model predictive control (qLPV-MPC) scheme, exploiting the beneficial (convex) properties of a qLPV system description. The qLPV model is obtained by a demodulation transformation and is subsequently augmented with a simple wind turbine model. Results show the effectiveness of the algorithm in synthetic and realistic simulations using the NREL 5-MW reference wind turbine in high-fidelity simulation code. Prolonged rotor speed operation at the tower side-side natural frequency is prevented, whereas when the trade-off is in favor of energy production, the algorithm decides to rapidly pass over the natural frequency to attain higher rotor speeds and power productions. KEYWORDS model demodulation transformation, model predictive control, quasi-linear parameter varying, tower natural frequency skipping INTRODUCTIONThe tower makes up a substantial part of the total turbine capital costs, and therefore finding an optimum between its mass and manufacturing expenses is a critical trade-off. 1 For conventional towers, diameters are limited because of land-based transportation constraints. This aspect dictates the increase of wall thickness for the production of taller towers and consequently leads to increased weight and costs. Conventional tower designs are soft-stiff to locate the tower fundamental frequency outside the turbine variable-speed operational range and thereby eliminate the possibility of exciting a tower resonance by the rotor rotational or blade-passing frequency. However, with the ever increasing wind turbine power rates, a combination of technical solutions should enable future, low-cost, tall towers, by relaxing this frequency constraint. Soft-soft tower configurations form an opportunity for tall towers by their smaller tower diameters and reduced wall thickness. As a result, soft-soft towers are
Abstract. Wind energy research groups from various disciplines generally use self-developed baseline wind turbine control implementations and tunings, which complicates the evaluation and comparison of new control algorithms. To solve this problem, the Delft Research Controller (DRC) provides an open, modular and fully adaptable baseline wind turbine controller to the scientific community. New control implementations can be added to the existing baseline controller, and in this way, convenient assessments of the proposed algorithms is possible.Because of the open character and modular set-up, scientists are able to collaborate and contribute in making continuous improvements to the code. The DRC is being developed in Fortran and uses the Bladed-style DISCON controller interface. The compiled controller is configured by a single control settings parameter file, and can work with any wind turbine model and simulation software using the DISCON interface. Baseline parameter files are supplied for the NREL 5-MW and DTU 10-MW reference wind turbines.
The estimation of the rotor effective wind speed is used in modern wind turbines to provide advanced power and load control capabilities. However, with the ever increasing rotor sizes, the wind field over the rotor surface shows a higher degree of spatial variation. A single effective wind speed estimation therefore limits the attainable levels of load mitigation, and the estimation of the blade effective wind speed (BEWS) might present opportunities for improved load control. This letter introduces two novel BEWS estimator approaches: a proportional-integral-notch (PIN) estimator based on individual blade load measurements, and a Coleman estimator targeting the estimation in the nonrotating frame. Given the seeming disparities between these two estimators, the objective of this letter is to analyze the similarities between the approaches. It is shown that the PIN estimator, which is equivalent to the diagonal form of the Coleman estimator, is a simple but effective method to estimate the BEWS. The Coleman estimator, which takes the coupling effects between individual blades into account, shows a more well-behaved transient response than the PIN estimator.
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