Wakes of upstream turbines impinge on downstream turbines in wind farms, causing power losses and increased fatigue. Wind farm control methods, such as the Helix approach, have been proposed to actively stimulate mixing of the wake with the free stream by pitching the blades dynamically. As a result, a periodic structure is forced in the wake, which increases average downstream wind velocity and thereby improves downstream turbines’ power production. However, downstream turbines could further exploit this periodic wake structure by pitching dynamically as well, but in sync with the phase of the incoming wake structure. Depending on the phase offset between the impinging wake and the downstream pitch, this creates destructive or constructive interference between the two wakes and further improves power production downstream. This work presents and experimentally validates such a control strategy for downstream wind turbines and evaluates it on a three-turbine wind farm in an experimental wind tunnel setting using scaled wind turbines. Results validate the controller's effectiveness and show that the third turbine's performance improvement is strongly influenced by the phase offset between the periodic wake components generated by the second turbine and those present in the upstream wake.

Subspace predictive control (SPC) is a data-driven control strategy that utilizes input–output measurements to predict future system behavior without requiring explicit model identification. Conventional SPC exhibits vulnerability to an unknown input disturbance, leading to degraded control performance and steady-state errors. To address these limitations, this article proposes a robust SPC method that inherently mitigates the effect of a constant input disturbance by augmenting the state-space representation through the internal model principle (IMP). This augmentation enables the controller to achieve integral action without requiring a separate disturbance observer (DOB) design. The proposed method is implemented in a data-driven framework, where an auxiliary disturbance is introduced into the data-driven algorithm to enhance disturbance rejection. A transfer function analysis verifies that the proposed Robust SPC eliminates a constant disturbance while maintaining the role of a DOB. Experimental validation on a two-inertia system confirms that the proposed method significantly improves reference tracking performance compared to conventional SPC, demonstrating its effectiveness in disturbance rejection without additional modeling complexity.

The behavior of a linear time-invariant system can be characterized entirely by measured input–output data that spans the vector space of all possible trajectories of the system relying on the fundamental lemma by Willems et al. However, useful a priori knowledge of the system is usually neglected. We propose a novel method for incorporating prior knowledge, specifically, known pole and zero locations, into a data-driven representation by constructing filters that pre-process the measured input–output data. To this end, a physics-informed data-driven predictor is introduced, where trajectories are obtained as linear combinations of the columns of a filtered block-Hankel matrix. We explicitly derive the output prediction error and show how leveraging prior knowledge reduces the impact of future noise realizations on output predictions and improves the accuracy of the initial state that is inferred from past data.

Engineering wake models are essential tools in wind farm design and operation. Their computational efficiency enables rapid layout optimization, energy calculation, and turbine control setpoint design. As wind farms grow in scale and density, wake interactions between turbines limit overall energy capture and increase structural loading. Wind Farm Control strategies have become critical for mitigating these effects. While wake steering, the intentional yaw misalignment of upstream turbines, has consistently demonstrated power gains, new dynamic control strategies are emerging. Among these, the Helix approach has shown particular promise in accelerating wake recovery and improving downstream power production. While engineering models for wake steering are well established, dedicated models for the Helix approach remain unavailable. This study presents a novel steady-state engineering model for predicting the velocity deficit and added turbulence of wind turbines operating under Helix actuation. The proposed model extends double-Gaussian velocity deficit and bell-curve– shaped turbulence intensity formulations to account for Helix-specific effects of actuation amplitude and ambient turbulence. Calibration and validation against high-fidelity large-eddy simulations demonstrate strong agreement across a wide range of operating conditions. The model accurately reproduces wake recovery trends, variations in the velocity-deficit profile, turbulence distributions, and the resulting downstream power availability. Finally, a case study on a large offshore wind farm illustrates that, under typical offshore atmospheric conditions, Helix control and wake steering yield individual power gains of up to 2.5% and 6.1%, respectively, while their combined application achieves total power gains of up to 7.1%.

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.

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.

Collective pitch control (CPC) and individual pitch control (IPC) are key strategies for power regulation and structural load mitigation in large offshore wind turbines operating in the nominal region. CPC and IPC operate in different frequency ranges, with CPC regulating power and IPC mitigating asymmetric blade loads. CPC tuning presents a significant trade-off, as improving power regulation often increases blade fatigue loads. IPC is typically implemented using the multiblade coordinate (MBC) transformation, which assumes decoupling between tilt and yaw control loops. However, practical implementations exhibit residual coupling effects. Advanced IPC schemes, such as azimuth offset compensation and static inverted decoupling, have been proposed to mitigate this interaction, but systematic comparative performance evaluations remain limited. This study proposes a multi-objective optimisation framework for the simultaneous tuning of CPC and IPC parameters to assess the trade-off between blade fatigue reduction and power regulation performance. Four gain-scheduled CPC-IPC configurations, including advanced IPC implementations, were implemented on a 15 MW wind turbine simulated using OpenFAST. Results show that simultaneous tuning of conventional CPC–IPC expands the achievable performance space, achieving average improvements of approximately 7–8% in blade fatigue loads and 23–24% in power regulation compared to the ROSCO–IPC baseline controller. Azimuth offset and static inverted decoupling IPC configurations exhibit comparable load–power performance, achieving reductions of approximately 17–19% in blade fatigue loads and 29–30% in power regulation relative to the ROSCO–IPC baseline, with only a moderate increase in pitch actuator activity of approximately 3–3.5%.

In the pursuit of mitigating the wake effect, floating wind turbines have additional degrees of freedom compared to their fixed-bottom counterparts. The mooring system with which floating wind turbines are anchored to the seabed allows a range of motion in which turbines can be repositioned. Turbine repositioning uses yaw control to reposition floating wind turbines, and to thereby actively optimize the wind farm layout. Previous research has focused on obtaining optimal steady-state yaw angles for turbine repositioning by using steady-state wake models. Here, the primary conclusion is that mooring line tension needs to be relaxed to facilitate a range of movement large enough for steady-state turbine repositioning to be effective. The presented work studies the effect of using dynamic yaw signals for turbine repositioning by using a dynamic wake model. To study the effect of including wake dynamics, an optimization problem to find the optimal yaw control signals for a two turbine floating wind farm is solved for various mooring configurations. This work shows that for stiffer mooring configurations, turbine repositioning can still be leveraged to increase wind farm efficiency, but that the optimal yaw control action is dynamic for these cases.

Periodic wakes are created on upstream wind turbines by pitching strategies, such as the Helix approach, to enhance wake mixing and thereby increase power production for wind turbines directly in their wake. Consequently, a cyclic load is not only generated on the actuating turbine’s blades but also on the waked wind turbine. While the upstream load is the result of the pitching required for wake mixing, the downstream load originates from interaction with the periodic wake and only causes fatigue damage. This study proposes two novel individual pitch control schemes in which such a periodic load on the downstream turbine can be treated: by attenuation or amplification. The former method improves the fatigue life of the downstream turbine, whereas the latter enhances wake mixing further downstream by exploiting the already-present periodic content in the wake; both were validated on a three-turbine wind farm in high-fidelity large-eddy simulations. Fatigue damage reductions of around 10% were found in the load mitigation case, while an additional power enhancement of 6% was generated on the third turbine when implementing the amplification strategy. Both objectives can easily be toggled depending on a wind farm operator’s demands and the desired loads/energy capture tradeoff.

High penetration of wind energy is pushing wind farms (WFs) to offer grid support capabilities, such as active power tracking. One of the main challenges in active power tracking for WFs is the interaction of wind turbines (WTs) through their wakes. This reduces the available wind in downstream WTs, leading them to saturation, while also affecting structural loading. With the increasing number of WTs in individual WFs, the computational and communication complexity of implementing centralized control architectures grows, posing challenges for real-world applications. In this article, we present a novel distributed control approach for active power tracking for WFs, namely multirate consensus-based distributed control (MCDC). The MCDC is designed to ensure that tracking errors caused by WT saturation are equally compensated throughout the WF, while only requiring local information exchanges between WTs. Furthermore, the proposed controller ensures that WT aerodynamic loading is balanced across the WF in a distributed manner. Finally, the overall power reference is distributed via a leader–follower consensus algorithm, resulting in a fully distributed approach. Our control approach facilitates the WF modularity and sparsity, which reduces the costs associated with control design and its applicability. Throughout this article, we demonstrate the effectiveness of the proposed MCDC through high-fidelity simulations, presenting performance comparable to the centralized control.