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.

To justify the use of two single-input single-output (SISO) control loops instead of more complex multi-input multi-output (MIMO) control, the axes in a wind turbine's pitch control system should be fully decoupled using the multi-blade coordinate transform. To achieve that, usually, an azimuth offset is required, correcting for phase lags originating from, e.g., actuator delays and blade flexibility. In wind turbine simulations, this parameter is commonly obtained by analysis of the linearized turbine models. This work, however, demonstrates that analyzing linearized turbine models is not sufficient for correcting the full phase lag when coupling wind turbine simulation tools to large-eddy simulators (LES), since additional phase lags may arise. Instead, this work proposes deriving the azimuth offset using data-driven modelling directly in coupled LES, where data is generated by exciting the structure with pseudo-random binary noise. Using this approach it was found that the optimal azimuth offset is three degrees higher than when using the linearized model, which demonstrates that deriving the optimal azimuth offset from linearized models is not suitable for coupled simulations.

Economic model predictive control (EMPC) has received increasing attention in the wind energy community due to its ability to trade-off economic objectives with ease. However, for wind turbine applications, inherent nonlinearities, such as from aerodynamics, pose difficulties in attaining a convex optimal control problem (OCP), by which real-time deployment is not only possible but also a globally optimal solution is guaranteed. A variable transformation can be utilized to obtain a convex OCP, where nominal variables, such as rotational speed, pitch angle, and torque, are exchanged with an alternative set in terms of power and energy. The ensuing convex EMPC (CEMPC) possesses linear dynamics, convex constraints, and concave economic objectives and has been successfully employed to address power control and tower fatigue alleviation. This work focuses on extending the blade loads mitigation aspect of the CEMPC framework by exploiting its individual pitch control (IPC) capabilities, resulting in a novel CEMPC-IPC technique. This extension is made possible by reformulating static blade and rotor moments in terms of individual blade aerodynamic powers and rotational kinetic energy of the drivetrain. The effectiveness of the proposed method is showcased in a mid-fidelity wind turbine simulation environment in various wind cases, in which comparisons with a basic CEMPC without load mitigation capability and a baseline IPC are made.

On-shore horizontal-axis wind turbines (HAWTs) provide a cost-effective solution for low carbon electricity generation. However, public acceptance is still a problem. A possible alternative to a HAWT is a vertical-axis wind turbine (VAWT), which is more visually appealing and less noisy. Furthermore, the inherent omni-directionality of VAWTs makes them suitable for installation in urban environments where the turbulence levels are high, and the wind direction variations are significant. However, the variation with the azimuth angle of the blade-effective wind speed and the angle of attack makes VAWT performance difficult to predict. This study proposes a wind speed estimator for a VAWT to address this challenge and to exploit knowledge of the blade-effective wind speed for load reduction control strategies. An Unscented Kalman Filter is used to solve the blade-effective wind speed estimation problem and is applied to a realistic 1.5 m two-bladed H-Darrieus VAWT model, for which the aerodynamic characteristics are determined using an actuator cylinder model. The system performance is evaluated using different wind speed variation scenarios. Overall, good agreement between the reference and estimated blade-effective wind speed is found both in terms of trend and absolute values.

Wind farm controllers such as the Helix approach have shown potential in increasing plant power production through wake mixing. The concept suggests that actuating the upstream turbines' blade pitching with a specific periodic signal can induce a helix-shaped wake, thereby alleviating wind velocity deficit on downstream turbines. Wake mixing initiation by downstream turbines may also be shown advantageous for power production; however, little to no attention has been given to such an approach. Similar wake mixing is expected to be achievable at lower control costs if the downstream turbine can benefit from the periodic component already present in the wake of the upstream turbine. Such a hypothesis is studied in this work by designing a minimal control scheme where the wake acting on the downstream turbine is simulated by a periodic input disturbance. A Kalman filter is proposed for incoming input disturbance phase estimation using SCADA data. The reconstructed phase information allows synchronization of the downstream control action with the periodic input disturbance by means of a phase synchronization wake mixing controller. The periodic component was estimated with a minimal root-mean-square error and the resulting control action was in phase with the input disturbance and demonstrated satisfactory performance even with a small phase perturbation. Future work will include applications in a high-fidelity wind turbine model and wind tunnel studies.

The Immersion and Invariance (II) wind speed estimator is a powerful and widely-used technique to estimate the rotor effective wind speed on horizontal axis wind turbines. Anyway, its global convergence proof is rather cumbersome, which hinders the extension of the method and proof to time-delayed and/or uncertain systems. In this letter, we illustrate that the circle criterion can be used as an alternative method to prove the global convergence of the II estimator. This also opens up the inclusion of time-delays and uncertainties. First, we demonstrate that the II wind speed estimator is equivalent to a torque balance estimator with a proportional correction term. As the nonlinearity in the estimator is sector bounded, the well-known circle criterion is applied to the estimator to guarantee its global convergence for time-delayed systems. By looking at the theoretical framework from this new perspective, this letter further proposes the addition of an integrator to the correction term to improve the estimator performance. Case studies show that the proposed estimator with an additional integral correction term is effective at wind speed estimation. Furthermore, its global convergence can be guaranteed by the circle criterion for time-delayed systems.

The estimation of the rotor effective wind speed is used in modern wind turbines to provide advanced power and load control capabilities. However, with the ever increasing rotor sizes, the wind field over the rotor surface shows a higher degree of spatial variation. A single effective wind speed estimation therefore limits the attainable levels of load mitigation, and the estimation of the blade effective wind speed (BEWS) might present opportunities for improved load control. This letter introduces two novel BEWS estimator approaches: a proportional-integral-notch (PIN) estimator based on individual blade load measurements, and a Coleman estimator targeting the estimation in the nonrotating frame. Given the seeming disparities between these two estimators, the objective of this letter is to analyze the similarities between the approaches. It is shown that the PIN estimator, which is equivalent to the diagonal form of the Coleman estimator, is a simple but effective method to estimate the BEWS. The Coleman estimator, which takes the coupling effects between individual blades into account, shows a more well-behaved transient response than the PIN estimator.

The wind turbine side-side tower motion is known to be lightly damped. One viable active damping solution is realized by deploying individual pitch control (IPC) such that counteracting blade forces are created to alleviate the tower fatigue loading caused by this motion. Existing IPC methods for side-side tower damping in the literature, such as linear quadratic regulator and lead-lag controller, cannot accommodate direct optimization and tradeoff tunings of the wind turbine economic performance. In this work, a novel side-side tower damping IPC strategy under a convex economic model predictive control (CEMPC) framework is therefore developed to address these challenges. The main idea of the framework lies in the variable transformation in power and energy terms to obtain linear dynamics and convex constraints, over which the economic performance of the wind turbine is maximized with a globally optimal solution in a receding horizon manner. The effectiveness of the proposed method is showcased in a high-fidelity simulation environment under both steady and turbulent wind cases. Lower fatigue damage on the side-side tower bending moment is attained with an acceptable level of pitch activities, negligible impact on the blade loads, and minor improvement on the power production.

With the trend of increasing wind turbine rotor diameters, the mitigation of blade fatigue loadings is of special interest to extend the turbine lifetime. Fatigue load reductions can be partly accomplished using individual pitch control (IPC), and is commonly facilitated by the so-called multiblade coordinate (MBC) transformation. This operation transforms and decouples the blade load signals in a non-rotating yaw-axis and tilt-axis. However, in practical scenarios, the resulting transformed system still shows coupling between the axes. To cope with this phenomenon, earlier research has shown that the introduction of an additional MBC tuning variable-the azimuth offset-decouples the multivariable system. However, the introduction of this extra variable complicates the controller design process, and requires expert knowledge and specialized analysis software. To provide an efficient method for the optimization of fixed-structure IPC controllers, based on black box and computationally costly objective functions, this paper considers a Bayesian optimization controller tuning framework. Results show the efficiency of the framework to tune a combined 1P + 2P IPC implementation, without prior knowledge, and based on high-fidelity simulation results using a computationally expensive objective function.