Trailing edge aps located outboard on wind turbine blades have recently shown considerable potential in the alleviation of turbine lifetime dynamic loads. The concept of the free-oating ap is speci_cally interesting for wind turbines, on account of its modularity and enhanced control authority. Such a ap is free to rotate about its axis; camberline control of the free-oating ap allows for aeroelastic control of blade loads. This paper describes the design of a scaled wind turbine blade instrumented with free-oating aps, intended for use in wind tunnel experiments. The nature of the ap introduces a coupled form of utter due to the aeroelastic coupling of ap rigid-body and blade out-of-plane modes; for maximal control authority it is desired to operate close to the utter limit. Analytical and numerical methods are used to perform a utter analysis of the turbine blade. It is shown that the potential ow aeroelastic model can be recast as a continuous-time Linear-Parameter-Varying (LPV) state space model of a low order, for which formal controller design methodologies are readily available. @en

Simulations of a stiff rotor configuration of the DTU 10MW Reference Wind Turbine are performed in order to assess the impact of prescribed flap motion on the aerodynamic loads on a blade sectional and rotor integral level. Results of the engineering models used by DTU (HAWC2), TUDelft (Bladed) and NTUA (hGAST) are compared to the CFD predictions of USTUTT-IAG (FLOWer). Results show fairly good comparison in terms of axial loading, while alignment of tangential and drag-related forces across the numerical codes needs to be improved, together with unsteady corrections associated with rotor wake dynamics. The use of a new wake model in HAWC2 shows considerable accuracy improvements.@en

Wind turbine load alleviation has traditionally been addressed in the literature using either full-span pitch control, which has limited bandwidth, or trailing-edge flap control, which typically shows low control authority due to actuation constraints. This paper combines both methods and demonstrates the feasibility and advantages of such a combined control strategy on a scaled prototype in a series of wind tunnel tests. The pitchable blades of the test turbine are instrumented with free-floating flaps close to the tip, designed such that they aerodynamically magnify the low stroke of high-bandwidth actuators. The additional degree of freedom leads to aeroelastic coupling with the blade flexible modes. The inertia of the flaps was tuned such that instability occurs just beyond the operational envelope of the wind turbine; the system can however be stabilised using collocated closed-loop control. A feedforward controller is shown to be capable of significant reduction of the deterministic loads of the turbine. Iterative feedforward tuning, in combination with a stabilising feedback controller, is used to optimise the controller online in an automated manner, to maximise load reduction. Since the system is non-linear, the controller gains vary with wind speed; this paper also shows that iterative feedforward tuning is capable of generating the optimal gain schedule online.@en

For a time-varying plant operating in closed-loop with a stabilising controller, rapid changes in system dynamics can be detected online using recursive subspace identification methods to estimate the open-loop system behaviour. However, these methods usually involve a speed-accuracy trade-off: accurate identification can often only be achieved by slow updates, which increases the lag in the detection of changes in system dynamics. In this paper, a closed-loop, recursive subspace identification algorithm is extended with a convex cost function based on the nuclear norm. The nuclear norm heuristic exploits structure in the algorithm by enforcing a low-rank condition on the state predictor matrix. This condition reduces the variance of the estimates at the price of introducing a bias. The new algorithm is demonstrated for a system where the damping changes from positive to negative, and it is shown to successfully and consistently estimate the onset of open-loop instability, outperforming conventional recursive identification. Further, by tuning the forgetting factor in the estimation algorithm, a favourable speed-accuracy trade-off can be achieved.@en

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. @en

This paper presents an experimental aeroservoelastic investigation of a novel load al-leviation concept using trailing edge aps. These aps are autonomous units, which are self-powered and self-actuated, using trailing edge tabs, thereby demonstrating advantages in comparison with conventional ap systems in terms of wiring and structural integration. The aps are free-oating and mass underbalanced, such that they may utter at operation velocities unless suppressed by their own control system. This makes the system very re-sponsive for turbulence and control action. In the wind tunnel campaign presented in this paper, the limit cycle behavior of autonomous, free-oating aps was investigated. It has been shown that limit cycle oscillation can be reached either through structural limiters or by control actions of the trailing edge tabs. In the latter case, the amplitude of the limit cycle oscillation is adjustable to the required energy output. An energy balance of har-vested power and power consumption for actuators and sensing system was made showing that the vibration energy of limit cycle oscillations can be used to keep the amplitude of the limit cycle constant, while the electric batteries that power the load alleviation system are being charged.@en

Wind turbines are fatigue critical machines for which the problem of vibrations is becoming predominant as rotor sizes keep increasing. Whereas the load reduction potential of wind turbine smart blades with trailing-edge control devices is now well-recognised, the design of load alleviation control systems which are efficient, robust and yet simple remains an open problem. The present paper evaluates the efficiency of a new proposed decoupled loop-shaping control (DLSC) strategy for the load alleviation of wind turbine blades equipped with multiple control surfaces. The efficiency of the proposed decoupling control strategy is evaluated against the well-established model predictive controller (MPC). For that purpose, an in-house wind turbine aeroelastic control (WTAC) simulator is employed to simulate the aeroelastic dynamics of the NREL 5 MW wind turbine operating in a fully turbulent environment. WTAC incorporates a wind field generator, an aerodynamic model and a finite element structural blade model. Trailing edge flaps are used as active control surfaces and are dynamically coupled to the aeroelastic wind turbine blade models. First, a state-of-the-art MPC is designed to address the load alleviation control problem. MPC is specifically chosen on account of its multi-input multi-output (MIMO) nature and constraints handling capabilities. Although high load alleviation efficiency is achieved, the MPC remains a complex timebased state controllers. Next, the proposed frequency-based loop-shaping controller is applied to the same control problem. The DLSC takes advantages of the dynamic response of blades to reformulate the MIMO load alleviation control problem as decoupled single-input single-output (SISO) control loops. Classic loop-shaping control theory is then used to design SISO controllers. The DLSC performance is compared with that of the MPC based on 1P blade root load bending moment reduction. The results of this investigation demonstrate the efficiency of the proposed DLSC strategy with respect to the MPC controller. The advantageous simplicity and insightful features of the DLSC are also highlighted.@en