• Energy Research
• 2021-2025close

AbstractThis paper presents an experimental assessment of a blended fatigue‐extreme controller for load control employing trailing edge flaps on a lab‐scale wind turbine. The controller blends between a repetitive model predictive controller that targets fatigue loads and a dedicated extreme load controller, which consists of a simple on‐off load control strategy. The Fatigue controller uses the flapwise blade root bending moments of the three blades as input sensors. The Extreme controller additionally uses on‐blade angle of attack and velocity measurements as well as acceleration measurements to detect extreme events and to allow for a fast reaction. The experiments are conducted on the Berlin Research Turbine within the large wind tunnel of the TU Berlin. In order to reproduce test cases with deterministic extreme wind conditions that follow industry standards, the wind tunnel was redesigned. The analyzed test cases are extreme direction change, extreme coherent gust, extreme operating gust and extreme coherent gust with direction change. The test cases are analyzed by on‐blade angle of attack and velocity measurements. The load control performance of the Blended controller is compared to the pure fatigue oriented and the pure extreme load controller. The Blended controller achieves a maximum flapwise blade root bending moment reduction of 23%, which is comparable to the reduction achieved by the Extreme controller.

Abstract The design of a three-bladed horizontal axis research wind turbine for wake investigations is described. The turbine has a rotor diameter of 1.3 m and will be operated in a large water towing tank at a submergence depth of 2.5 m, yielding 3.3 % blockage. The test rig is configured to allow for underwater-stereo-particle-image-velocimetry measurements of the near and the far wake at chordwise Reynolds numbers approaching 700 000. The low-Reynolds number airfoil SG6040 is selected to constitute the rotor blades. Cavitation-free operation is assured by assigning a rated angle of attack of 1°. The blades are designed to maintain a constant circulation along the blade span and to minimize tip deflections. The blade pitch angles are adjustable. Various blade designs, with and without passive flow control devices, can be tested by replacing individual blade sections. This eliminates the need for multiple sets of blades. Sensors for acquiring the turbine’s thrust, torque, rotational speed, the azimuthal positions of the blades, and the imposed blade root bending moments are incorporated into the design. The wind turbine simulation suite QBlade is utilized to simulate the turbine characteristics. A model of the entire test rig is derived, representing its structural properties. Results from the analytical verification of the structural model are provided. QBlade is utilized to analyze the test rig design by imposing design loads and calculating the modal properties.

The potential of airfoil optimisation for the specific requirements of airborne wind energy (AWE) systems is investigated. Experimental and numerical investigations were conducted at high Reynolds numbers for the S1223 airfoil and an optimised airfoil with thin slat. The optimised geometry was generated using the NSGA-II optimisation algorithm in conjunction with 2D-RANS simulations. The results showed that simultaneous optimisation of the slat and airfoil is the most promising approach. Furthermore, the choice of turbulence model was found to be crucial, requiring appropriate transition modeling to reproduce experimental data. The k-ω-SST-γ-Reθ model proved to be most suitable for the geometries investigated. Wind tunnel experiments were conducted with high aspect ratio model airfoils, using a novel structural design, relying mostly on 3D-printed airfoil segments. The optimised airfoil and slat geometry showed significantly improved maximum lift and a shift of the maximum power factor to higher angles of attack, indicating good potential for use in AWE systems, especially at higher Reynolds numbers. The combined numerical and experimental approach proved to be very successful and the overall process a promising starting point for future optimisation and investigation of airfoils for AWE systems.

SummaryThis paper presents the results of an advanced control strategy that employs active trailing edge flaps to reduce the fatigue loads of an experimental wind turbine. The strategy, called repetitive model predictive control, is a multiple‐input multiple‐output controller that aims at the alleviation of out‐of‐plane blade root bending moments. The strategy incorporates the control commands, output errors, and state deviation from the previous rotation. This way, the time lag in the strain sensor input due to the blade inertia is compensated. Additionally, a strategy to limit the computational costs is presented. The load alleviation performance is evaluated at different yaw cases and compared with different individual flap control strategies. The repetitive model predictive control is able to reduce the fatigue loads by up to 23% compared with the better performing individual flap control strategy. This improvement in load reduction is accompanied by an increase in flap travel of up to 7% compared with the individual flap control strategies.