• Energy Research
• 2021-2025close
• Wind Energy Science close
• Netherlands close

Abstract. Individual pitch control (IPC) is a well-known approach to reduce blade loads on wind turbines. Although very effective, IPC usually requires high levels of actuator activities, which significantly increases the pitch actuator duty cycle (ADC). This will subsequently result in an increase of the wear on the bearings of the blades and a decrease of the wind turbine reliability. An alternative approach to this issue is to reduce the actuator activities by incorporating the output constraints in IPC. In this paper, a fully data-driven IPC approach, which is called constrained subspace predictive repetitive control (cSPRC), is introduced. The output constraints can be explicitly considered in the control problem formulation via a model predictive control (MPC) approach. The cSPRC approach will actively produce the IPC action for the necessary load reduction when the blade loads violate the output constraints. In this way, actuator activities can be significantly reduced. Two kinds of scenarios are simulated to illustrate the unique applications of the proposed method: wake–rotor overlap and turbulent sheared wind conditions. Simulation results show that the developed cSPRC is able to account for the output constraints into the control problem formulation. Since the IPC action from cSPRC is only triggered to prevent violating the output constraints, the actuator activities are significantly reduced. This will help to reduce the pitch ADC, thus leading to an economical viable load control strategy. In addition, this approach allows the wind farm operator to design conservative bounds to guarantee the safety of the wind turbine control system.

Abstract. Individual pitch control (IPC) is a well-known approach to reduce blade loads on wind turbines. Although very effective, IPC usually requires high levels of actuator activities, which significantly increases the pitch actuator duty cycle (ADC). This will subsequently result in an increase of the wear on the bearings of the blades and a decrease of the wind turbine reliability. An alternative approach to this issue is to reduce the actuator activities by incorporating the output constraints in IPC. In this paper, a fully data-driven IPC approach, which is called constrained subspace predictive repetitive control (cSPRC), is introduced. The output constraints can be explicitly considered in the control problem formulation via a model predictive control (MPC) approach. The cSPRC approach will actively produce the IPC action for the necessary load reduction when the blade loads violate the output constraints. In this way, actuator activities can be significantly reduced. Two kinds of scenarios are simulated to illustrate the unique applications of the proposed method: wake–rotor overlap and turbulent sheared wind conditions. Simulation results show that the developed cSPRC is able to account for the output constraints into the control problem formulation. Since the IPC action from cSPRC is only triggered to prevent violating the output constraints, the actuator activities are significantly reduced. This will help to reduce the pitch ADC, thus leading to an economical viable load control strategy. In addition, this approach allows the wind farm operator to design conservative bounds to guarantee the safety of the wind turbine control system.

Abstract. The flexible-membrane kite employed by some airborne wind energy systems uses a suspended control unit, which experiences a characteristic swinging motion relative to the top of the kite during sharp turning manoeuvres. This paper assesses the accuracy of a two-point kite model in resolving this swinging motion using two different approaches: approximating the motion as a transition through steady-rotation states and solving the motion dynamically. The kite is modelled with two rigidly linked point masses representing the control unit and wing, which conveniently extend a discretised tether model. The tether-kite motion is solved by prescribing the trajectory of the wing point mass to replicate a figure-eight manoeuvre from the flight data of an existing prototype. The computed pitch and roll of the kite are compared against the attitude measurements of two sensors mounted to the wing. The two approaches compute similar pitch and roll angles during the straight sections of the figure-eight manoeuvre and match measurements within 3°. However, during the turns, the dynamically solved pitch and roll angles show systematic differences compared to the steady-rotation solution. As a two-point kite model resolves the roll, the lift force may tilt along with the kite, which is identified as the driving mechanism for turning flexible kites. Moreover, the two-point kite model complements the aerodynamic model as it allows for computing the angle of attack of the wing by resolving the pitch. These characteristics improve the generalisation of the kite model compared to a single-point model with little additional computational effort.

Abstract. The flexible-membrane kite employed by some airborne wind energy systems uses a suspended control unit, which experiences a characteristic swinging motion relative to the top of the kite during sharp turning manoeuvres. This paper assesses the accuracy of a two-point kite model in resolving this swinging motion using two different approaches: approximating the motion as a transition through steady-rotation states and solving the motion dynamically. The kite is modelled with two rigidly linked point masses representing the control unit and wing, which conveniently extend a discretised tether model. The tether-kite motion is solved by prescribing the trajectory of the wing point mass to replicate a figure-eight manoeuvre from the flight data of an existing prototype. The computed pitch and roll of the kite are compared against the attitude measurements of two sensors mounted to the wing. The two approaches compute similar pitch and roll angles during the straight sections of the figure-eight manoeuvre and match measurements within 3°. However, during the turns, the dynamically solved pitch and roll angles show systematic differences compared to the steady-rotation solution. As a two-point kite model resolves the roll, the lift force may tilt along with the kite, which is identified as the driving mechanism for turning flexible kites. Moreover, the two-point kite model complements the aerodynamic model as it allows for computing the angle of attack of the wing by resolving the pitch. These characteristics improve the generalisation of the kite model compared to a single-point model with little additional computational effort.

Abstract. This study presents findings from a wind tunnel experiment investigating a model wind turbine equipped with aft-swept blades. Utilising particle image velocimetry, velocity fields were measured at multiple radial stations. These allow the derivation of blade-level aerodynamic parameters, including bound circulation, induction values, inflow angle, angle of attack, and forces normal and tangential to the rotor plane. The measured local lift coefficient aligns well with the lift polar of the design airfoil, validating the experimental approach. The resulting public dataset provides a comprehensive aerodynamic characterisation of rotating swept blades in controlled conditions. It can serve as a baseline for future experimental research on swept wind turbine blades. Furthermore, it is valuable in validating numerical models of varying fidelity simulating swept wind turbine blades. The provided blade-level aerodynamics are particularly relevant to lower-fidelity models such as blade element momentum theory and lifting-line algorithms. At the same time, the measured flow fields can be compared against higher-fidelity simulation results from computational fluid dynamics.

Abstract. This study presents findings from a wind tunnel experiment investigating a model wind turbine equipped with aft-swept blades. Utilising particle image velocimetry, velocity fields were measured at multiple radial stations. These allow the derivation of blade-level aerodynamic parameters, including bound circulation, induction values, inflow angle, angle of attack, and forces normal and tangential to the rotor plane. The measured local lift coefficient aligns well with the lift polar of the design airfoil, validating the experimental approach. The resulting public dataset provides a comprehensive aerodynamic characterisation of rotating swept blades in controlled conditions. It can serve as a baseline for future experimental research on swept wind turbine blades. Furthermore, it is valuable in validating numerical models of varying fidelity simulating swept wind turbine blades. The provided blade-level aerodynamics are particularly relevant to lower-fidelity models such as blade element momentum theory and lifting-line algorithms. At the same time, the measured flow fields can be compared against higher-fidelity simulation results from computational fluid dynamics.

Abstract. Accurate knowledge of the input torque in wind turbine gearboxes is key to improving their reliability. Traditionally, rotor torque is measured using strain gauges bonded to the shaft. Transferring the resulting signal from the rotating shaft to a stationary data acquisition system while powering the sensing devices is complex and costly. The magnitude of the torques involved in wind turbine gearboxes and the high stiffness of the input shaft pose additional difficulties. This paper presents a new alternative method to measure the input torque in wind turbine gearboxes based on deformation measurements of the static first-stage ring gear. We have measured deformation using fiber-optic strain sensors based on fiber Bragg gratings because of their advantages compared to conventional electrical strain gauges. The present study was conducted on a Siemens Gamesa Renewable Energy gearbox with a rated power of 6 MW, in which a total of 54 fiber-optic strain sensors were installed on the outer surface of the first-stage ring gear. The gear mesh forces between the planets and the ring gear cause measurable deformations on the outer surface of the stationary ring gear. The measured strains exhibit a dynamic behavior. The strain values change depending on the relative position of the strain sensors to the planet gears, the instantaneous variations of the input torque, and the way load is shared between planets. A satisfactory correlation has been found between the strain signals measured on the static ring gear and torque. Two signal processing strategies are presented in this paper. The first procedure is based on the peak-to-peak strain values computed for the gear mesh events, and therefore torque can only be estimated when a gear mesh event is detected. The second signal processing procedure combines the strain signals from different sensors using a Coleman coordinate transformation and tracks the magnitude of the fifth harmonic component. With this second procedure, it is possible to estimate torque whenever strain data of all sensors are available, leading to an improved frequency resolution up to the sampling frequency used to acquire strain data. The method presented in this paper could make measuring gearbox torque more cost-effective, which would facilitate its adoption in serial wind turbines and enable novel data-driven control strategies, as well as a more accurate assessment of the consumed fatigue life of the gearboxes throughout their operation.