Wind energy has reached a high degree ofmaturity: for wind-rich onshore locations, it is already competitive with conventional energy sources. However, for low-wind, remote and offshore regions, research efforts are still required to enhance its economic viability. While it is po
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Wind energy has reached a high degree ofmaturity: for wind-rich onshore locations, it is already competitive with conventional energy sources. However, for low-wind, remote and offshore regions, research efforts are still required to enhance its economic viability. While it is possible to reduce the cost of energy by upscaling wind turbines, it is believed that we may be approaching a plateau in turbine size. Beyond this plateau, the material costs associated with the high dynamic turbine loads would outweigh the benefits of increased energy capture. To postpone this plateau, research is currently being carried out in the active control of loads for lightweight, flexible rotors. Traditional control for wind turbines involves the use of fixed-structure low order controllers, the gains of which are often hand-tuned separately for each turbine class. However, for the increasingly multivariable plant, such time-invariant approaches may no longer yield good performance. As such, the thesis focusses specifically on datadriven control for these flexible turbines. First, different data-driven approaches in the literature are evaluated and categorised as two-step approaches; which involves distinct online identification and control steps; and direct approaches, which uses data to iteratively tune fixed-structure controller gains. The approaches need to be modified to be made tractable in real time for implementation on wind turbines. For time-varying plants, such as wind turbines, it is often interesting to performidentification repeatedly over time for the two-step data-driven approach. Conventional recursive identification is extended in this thesis through the use of the nuclear norm. The benefit of the nuclear norm is evident in that it increases responsiveness of the algorithm, through the suppression of the effect of external noise. Identification can be readily combined with repetitive control for reducing periodic loads in the Subspace Predictive Repetitive Control (SPRC) technique. SPRC can be performed in a restricted basis function subspace, thus reducing the computational complexity and providing smooth control signals. The control law is stabilising and performs well as long as the identification converges to relatively good estimates, and the system dynamics change slowly. For varying wind speed, the approach above would require continuous reïdentification. As an alternative, a direct data-driven approach, Iterative Feedback Tuning (IFT) has been extended to gain-schedule tuning and for designing a Linear Parameter- Varying (LPV) controller for an LPV plant. This requires an exponential increase in the number of tuning experiments per iteration; however, structure can be used to reduce computational complexity. IFT-LPV converges to a locally optimal low-order controller. These data-driven approaches are evaluated for the load control of flexible rotors. A review of the state of the art shows that, for the low-frequency region of the load spectrum, full-span pitch control has demonstrable control authority. For higher frequencies, among the new actuators, it is found that trailing-edge flaps have the highest level of technological maturity. Aeroservoelastic simulations are carried out to show the potential of the data-driven approaches. SPRC is able to adaptively tune itself to achieve average blade load reductions close to those achieved by conventional approaches under similar conditions. For these load reductions the actuator duty is roughly half of that with the conventional approach. IFT-LPV has been used to tune a feedforward controller that works on similar basis functions scheduled on the azimuth. It can provide the correct control action irrespective of wind conditions. To expand the load control design space, pitch control is designed to stabilise an upwind turbine in yaw, without the yaw drive. This approach enables a trade-off between blade and support structure loads. SPRC is then investigated with wind tunnel experiments for pitch control of a scaled wind turbine. It reduces deterministic loads by over 60%with strict control over the pitch activity, and can also compensate for asymmetric blade control authority and changed operating conditions adaptively. Further, on this setup, the concept of IPC has been shown to perform yaw stabilisation for an upwind turbine for the first time. The setup blades are then redesigned to include free-floating trailing-edge flaps. First-principles models are set up for the system, and it is found that the system shows a low wind-speed form of flutter; this is validated experimentally. Recursive identification, using the nuclear norm, is able to track the unstable mode damping, and detect flutter twice as fast as conventional methods. Finally, a feedforward controller is tuned using IFT for combined pitch and flap control; the load peaks at 1P and 2P are almost entirely attenuated. IFT is also able to tune an linear gain schedule for operation across a range of wind speeds. It is concluded that iterative methods for data-driven control perform well for the highly uncertain control problem of flexible rotor load alleviation. For this, use has to be made of the structure of the problem. The two-step approach (such as SPRC), with combined recursive identification and control law synthesis, provides a convex first approximation of the desired controller. With the help of direct approaches, (like IFT-LPV), the controller structure can be reduced and fine-tuned to improve the control performance. Such quasi-feedforward data-driven approaches can complement the existing turbine control structure and achieve enhanced load control performance for flexible rotors.
