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.

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%.

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.

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.

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.

Denser turbine spacing in wind farms leads to increased wake interactions, causing power losses when each turbine operates under its own greedy control scheme. To mitigate these effects, research is exploring strategies that consider the entire wind farm rather than singular turbines. The so-called helix approach has recently gotten significant attention from the research community. It aims to reduce wake losses through periodic individual pitch control. Wake steering on the other hand uses yaw actuation to laterally deflect the wake away from downstream turbines. In this paper, we adapt and validate a steady-state surrogate model to compute the time-averaged velocity field behind a wind turbine operating with the helix approach. The model is tuned using data from Large Eddy Simulations. We compare the helix model to wake steering and baseline operation in a wind farm case study, demonstrating that the helix approach offers promising benefits under specific wind conditions.

Accurate knowledge of the mechanical loads of wind turbine gearboxes has become essential in modern, highly loaded gearbox designs, as maintaining or even improving gearbox reliability with increasing torque density demands is proving to be challenging. Unfortunately, the traditional method of measuring dynamic mechanical torque using strain gauges placed on the outer surface of a rotating shaft and transmitting the resulting signal is unsuitable for serial deployment due to technical and economic constraints. An alternative method based on fiber-optic strain sensors placed on the stationary outer surface of the gearbox ring gear has been proposed. Like shaft torsion, the radial deformation of the ring gear is proportionate to the rotor torque. Placing the sensors on a stationary component is a cost-effective alternative for serial implementation because the need for complex and expensive data transfer via wireless transmission or a slip ring is eliminated. In this paper, we present the results of an extensive field experiment conducted to evaluate the torque measurement accuracy of this novel sensing solution installed on the gearbox of a Gamesa G97 2-MW wind turbine at the National Renewable Energy Laboratory’s Flatirons Campus. Torque measurements derived from fiber-optic strain sensors placed on the ring gear of the planetary stage are compared to conventional torque measurements from strain gauges placed on the main shaft. Two different torque estimation data processing methods were evaluated, with the method based on operational deflection shapes providing the most accurate results with an average normalized root mean square error below 0.7% for a load revolution distribution analysis. The effect of operating conditions on the torque estimate was also investigated, and the third planet-passing operational deflection shape was found to be the least sensitive to nontorque load-related effects. The fiber-optic strain sensors’ successful operation during the complete test campaign has demonstrated a robust and accurate solution for fleet-wide enhanced gearbox remaining useful life estimation.

Floating offshore wind turbines pave the way to accessing deep-water regions with abundant wind resources. However, they face specific control challenges, such as the negative damping problem and increased model complexity. Since model-based control is becoming increasingly demanding, a model-free, data-driven approach is considered. Additionally, floating wind turbines are susceptible to rough environmental disturbances. Feedforward information, such as wave elevation measurements from wave radars, may be included in the controller to lessen the impact of disturbances. Although waves have been shown to increase rotor speed oscillations and turbine loads, wave-preview-based methods have only recently been explored. To this end, this paper first proposes a modified Data-enabled Predictive Control formulation that includes past and future information about measurable disturbances. The feasibility of this control strategy is then demonstrated for floating wind turbines through mid-fidelity simulations. The model-free, feedforward controller uses a preview of wave forces acting on the floating platform and aims for rotor speed regulation. Simulations indicate that the data-driven approach has potential for floating wind turbine control, and including wave feedforward action reduces the amplitude of rotor speed oscillations.