In the nominal operating region of wind turbines, collective pitch control (CPC) regulates power by maintaining rated rotor speed, while individual pitch control (IPC) mitigates cyclic blade loads caused by wind variations like wind shear or turbulence. However, tuning CPC presents a significant trade-off, as improving power regulation often leads to an increase in fatigue blade loads. Additionally, IPC implementation via Multi-Blade Coordinate (MBC) transformation suffers from coupling between IPC loops, reducing controller effectiveness. Prior studies suggest that azimuth offset and static inverted decoupling enhance classical IPC implementations by mitigating this coupling. However, a comparative performance analysis between both implementations remains unexplored. This study addresses this analysis through a multi-objective optimization approach to tune CPC and IPC strategies for a 15 MW reference wind turbine. Four configurations (baseline CPC with conventional IPC, CPC+IPC, CPC+IPC with azimuth offset, and CPC+IPC with static inverted decoupling) are optimized. Results show that a well-tuned CPC improves IPC effectiveness, while the incorporation of azimuth offset or static inverted decoupling in IPC significantly improves both objectives, achieving reductions of approximately 19% in damage equivalent load on the blades and 60% in the integral squared error of power output compared to the baseline CPC with conventional IPC, and around 9% and 30% relative to the CPC with conventional IPC.

Recent advancements have demonstrated that collectors based on the Coandă effect can effectively harvest polymetallic nodules from the seabed. However, the hydrodynamics of the flow around such collectors, particularly the mechanisms of ambient water entrainment, remain insufficiently explored. To address this gap, we performed three-dimensional numerical simulations to investigate the flow characteristics surrounding a Coandă effect-based collector, focusing on the effects of main jet velocity, secondary jet velocity, radius of curvature, and bottom clearance. The results show that increasing the main jet velocity enhances flow attachment and strengthens the pressure gradients beneath the collector, thereby increasing the entrainment of ambient water into the collection duct. Similarly, higher secondary jet velocities improve flow attachment and raise the collection duct flow rate but also lead to greater sideways water spillage. Furthermore, a larger radius of curvature reduces sideways spillage, consequently promoting greater ambient water entrainment beneath the collector. Likewise, increasing the bottom clearance enhances ambient water entrainment. Overall, these findings provide valuable insights for optimizing the operational parameters of Coandă effect-based collectors to maximize collection efficiency while minimizing water spillage.

Factors like growing data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In this paper we propose Closed-loop Data-enabled Predictive Control (CL-DeePC), a framework that unifies different approaches to address this challenge. To this end, CL-DeePC incorporates instrumental variables (IVs) to synthesize and sequentially apply consistent single or multi-step-ahead predictors. Furthermore, a computationally efficient CL-DeePC implementation is developed that reveals an equivalence with Closed-loop Subspace Predictive Control (CL-SPC). Time marching simulations of DeePC and CL-DeePC are conducted using Hankel matrices of past data that are updated at every time step to induce potentially troublesome closed-loop correlations between inputs and noise. Compared to DeePC, CL-DeePC simulations demonstrate superior reference tracking, with a sensitivity study finding a 48% lower susceptibility to noise-induced reference tracking performance degradation.

Wind turbines are getting larger to increase power capacity. Their longer blades sample a larger area of the spatially and temporally varying turbulent wind field, leading to increased periodic blade load and fatigue damage over time. Individual pitch control (IPC) has proven effective in alleviating these loads by pitching the blades. Conventional IPC fully attenuates the periodic blade loads, which requires excessive pitching, leading to additional stresses on the pitch system. To balance pitch actuation and load alleviation, bounds can be set on the pitch signal (input-constrained IPC), or on the load (output-constrained IPC). While input-constrained IPC has been abundantly researched, little research has focused on output-constrained IPC and on the trade-off when operating between full IPC and no IPC. Therefore, we propose an output-constrained IPC method using an adaptive leaky integrator. The natural frequency of the leaky integrator is adapted on the error between the reference and resultant blade moment. This allows the control scheme to attain every load alleviation level between full and no IPC. Furthermore, in realistic turbulent wind conditions, operating close to full IPC leads to diminishing returns, showing that the proposed controller achieves a superior trade-off between load reduction and actuator effort.

As wind turbine sizes and their rated power capacities increase, the spatial and temporal load imbalances over the rotor surface increase due to larger wind asymmetries, aerodynamic imbalances, and calibration offsets. The multiblade coordinate (MBC) transform-based individual pitch control (IPC) has garnered significant attention in the literature, and it considers loads in a non-rotating reference frame. This leads to increased pitch actuation for all wind turbine blades when subject to large load imbalances. On the contrary, the single-blade control (SBC) IPC strategy is well suited for handling such load imbalances as it involves equipping each blade with a localized control system, thereby ensuring an independent operation in a rotating reference frame. However, unlike MBC transform-based IPC, the effects of system phase lag on SBC performance have not been investigated. This article investigates the effects of such phase lags and multivariable coupling on SBC performance, and proposes a novel framework for phase compensation in SBC as a convenient method for constructing and calibrating a lead compensator. Using midfidelity OpenFAST simulations, it is demonstrated that phase compensation in SBC improves load mitigation at the targeted frequencies and reduces actuation effort. In contrast, the absence of phase compensation can lead to load amplifications, especially for larger wind turbines.

Individual pitch control (IPC) is a technique used to reduce periodic blade loads in wind turbines. It generally uses the multiblade coordinate transformation to convert blade load measurements from a rotating frame into a two-axes non-rotating frame. Although these non-rotating axes are assumed to be decoupled, studies reveal persistent interactions. Reducing this coupling, such as by introducing an azimuth offset, enhances IPC performance. This study explores the impact of static inverted decoupling, which decouples the process in the steady state, on IPC performance. The proposed IPCs are adaptive, scheduling controller and decoupling gains based on operational conditions. In such IPC designs, the integral gains of the diagonal controllers and the decoupling elements can either be the same or different. These methods were validated on a simulated 15 MW wind turbine. Controller parameter optimization was accomplished through genetic algorithms to minimize blade fatigue loads, measured via the damage equivalent load (DEL). Results indicate that incorporating static inverted decoupling into IPC improves blade load reduction without increasing pitch actuator effort. IPCs with similar integral gains and matching absolute values in decoupling elements achieved the best balance between DEL reduction and complexity with minimal actuator effort, while additional optimization parameters provided negligible improvements.

As wind turbine power capacities continue to rise, taller and more flexible tower designs are needed for support. These designs often have the tower's natural frequency in the turbine's operating regime, increasing the risk of resonance excitation and fatigue damage. Advanced load-reducing control methods are needed to enable flexible tower designs that consider the complex dynamics of flexible turbine towers during partial-load operation. This article proposes a novel modulation-demodulation control (MDC) strategy for side-side tower load reduction driven by the varying speed of the turbine. The MDC method demodulates the periodic content at the once-per-revolution (1P) frequency in the tower motion measurements into two orthogonal channels. The proposed scheme extends the conventional tower controller by augmentation of the MDC contribution to the generator torque signal. A linear analysis framework into the multivariable system in the demodulated domain reveals varying degrees of coupling at different rotational speeds and a gain sign flip. As a solution, a decoupling strategy has been developed, which simplifies the controller design process and allows for a straightforward (but highly effective) diagonal linear time-invariant (LTI) controller design. The high-fidelity OpenFAST wind turbine software evaluates the proposed controller scheme, demonstrating effective reduction of the 1P periodic loading and the tower's natural frequency excitation in the side-side tower motion.

Individual pitch control (IPC) is a method to mitigate periodic blade loads in wind turbines, and it is typically implemented using the multi-blade coordinate (MBC) transform, which converts the blade load measurements from a rotating frame into the non-rotating tilt axis and yaw axis. Previous studies have shown that by including an additional tuning parameter in the MBC, the azimuth offset reduces the coupling between non-rotating axes, allowing for higher performance levels for diagonal controller structures. In these studies, the decentralized control of IPC was composed of two identical integral controllers. This work analyzes and compares the improvement that the azimuth offset can provide in different adaptive gain scheduling IPCs where the diagonal controllers can have integral or proportional action with different gains. They are applied to a 15 MW wind turbine simulated with OpenFAST v3.5 software. The controller parameter tuning is addressed as an optimization that reduces blade fatigue load based on the damage equivalent load (DEL) and is resolved through genetic algorithms. Simulations show that only using different controller gains in IPC does not provide significant improvements; however, including azimuth offset in the optimal IPC schemes with integral controllers allows for the greatest DEL reduction with a lower actuator effort.

In order to mitigate periodic blade loads in wind turbines, recent research has analyzed different Individual Pitch Control (IPC) approaches, which typically use the multi-blade coordinate (MBC) transformation. Some of these studies show that the introduction of an additional tuning parameter in the MBC, namely the azimuth offset, helps to decouple the nonrotating axes in the MBC transformation and enhances the IPC performance. However, these improvements have been studied without considering the increased control effort performed by the pitch signal, which is the main negative side effect of the IPC. This work addresses this trade-off between pitch signal effort and blade fatigue reduction for IPC applied to a wind turbine operating in the full load region. Here, two IPC schemes, with and without additional azimuth offset, are designed and applied to a 15 MW monopile offshore wind turbine simulated with OpenFAST software. The optimal tuning of the IPC parameters is performed by means of a multi-objective optimization solved by genetic algorithms. The optimization procedure minimizes two objective functions related to pitch signal effort and blade fatigue load. The resulting Pareto fronts show a range of optimal solutions for each IPC scheme. The selected optimal solution for IPC with azimuth offset compared to the optimal solution for IPC without offset achieves improvements of more than 10% in blade load reduction maintaining similar pitch signal effort.

Wind turbine controllers are nowadays ever more advanced and rely on accurate internal controller model information. Therefore a calibrated model is needed for attaining predictable controller performance and ensuring stable operation. To calibrate the internal model information, a novel learning control scheme has recently been proposed that exploits the dynamics of the closed-loop controlled wind turbine system, without the need for wind speed measurements. The learning algorithm thereby periodically excites the generator power controller input signal. An extremum-seeking demodulation scheme was used to calibrate the internal model information. This paper improves the existing learning scheme in two ways: Firstly, it investigates how the frequency of the excitation signal influences the signal-to-noise ratio. Secondly, the problem was reformulated as a root-finding problem. This requires using the in-phase component of the phase-corrected learning signal. In addition, a precalculated lookup table relates the measured in-phase component directly to model uncertainty. It was found that an increased excitation frequency improves the signal-to-noise ratio (SNR) by an order of magnitude. Combined, these contributions improve the convergence speed more than twenty times, addressing the effect of aerodynamic degradation and its consequences on controller performance.

Wind turbines degrade over time, resulting in varying structural, aeroelastic, and aerodynamic properties. In contrast, the turbine controller calibrations generally remain constant, leading to suboptimal performance and potential stability issues. The calibration of wind turbine controller parameters is therefore of high interest. To this end, several adaptive control schemes based on extremum seeking control (ESC) have been proposed in the literature. These schemes have been successfully employed to maximize turbine power capture by optimization of the Kω²-type torque controller. In practice, ESC is performed using electrical generator power, which is easily obtained. This paper analyses the feasibility of torque gain optimization using aerodynamic and generator powers. It is shown that, unlike aerodynamic power, using the generator power objective limits the dither frequency to lower values, reducing the convergence rate unless the phase of the demodulation ESC signal is properly adjusted. A frequency-domain analysis of both systems shows distinct phase behavior, impacting ESC performance. A solution is proposed by constructing an objective measure based on an estimate of the aerodynamic power.

Advancements in wind turbine technology have made wind energy more cost-competitive. While taller towers use less material, they are more susceptible to fatigue. This study introduces a convex model predictive control scheme to actively counteract side-side periodic loads using a velocity-based approach, which captures the system's nonlinear behavior without requiring extensive prior operating points. A quasi-linear parameter-varying dynamic model for wind turbine towers is established through model demodulation transformation. Simulation results show a 96% reduction in net force in the side-side direction at the tower top under turbulent wind conditions.

Due to the increasing share of (offshore) wind turbines, more stringent requirements on power quality have been established. Importantly, the low-voltage ride-through grid requirement states that a wind turbine must remain connected to the electrical grid after a short intermittent grid fault. In the industry mainly gain-scheduled PID-controllers are used to mitigate the effects of grid faults on turbine operation, whereas more advanced solutions have been proposed in the literature such as model predictive control or multiple parallel PI-controllers. Remarkably, all controller implementations mentioned earlier are based on feedback control, where no feedforward strategies have been discussed in the literature. However, feedforward control could improve grid fault recovery performance by exploiting the relatively known fault characteristics by virtue of the specification in the Transmission System Operator requirements. Therefore, for the first time, a norm-optimal Iterative Learning Control (NO-ILC) algorithm is presented that solves these issues by learning the feedforward signal that optimally mitigates the effects of a grid fault. The NO-ILC algorithm applies model-free learning based on iterations, in which the framework of NO-ILC has been extended to include explicit input constraints. The goal of the NO-ILC is to reduce a (quadratic) cost function on specific input and output channels whilst conforming to specific input constraints by solving an optimisation problem, with, for this study blade pitch and rotor speed as respective input and output channels. It is shown that the NO-ILC algorithm can yield improved performance on a high-fidelity model, with a 45% decrease in the cost function used by NO-ILC compared to the nominal feedback control. The optimised feedforward signals resulting from NO-ILC can be used as an analysis tool to closer match the nominal grid fault feedback controllers response with that of NO-ILC, or directly applied as a library that can supplement the feedback controllers output during a grid fault.