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%.

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.

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.

Power losses at waked turbines due to the energy extraction of upstream turbines from the flow pose a major risk to the economic feasibility of wind farms. Helix active wake control has proven its potential to mitigate these wake-induced power losses by accelerating the recovery of the individual turbine wakes. This method leverages individual pitch control to induce a non-uniformly distributed force perturbation that rotates either in a clockwise (CW) or counterclockwise (CCW) direction around the rotor center. This deforms the wake into a helical shape that recovers faster than the wake of a conventionally controlled turbine. The CCW-oriented helix achieves higher power gains than the CW helix. Previous studies have identified a system of counter-rotating vortices to drive the wake recovery enhancement and the difference between CW and CCW helix. Nevertheless, a causal explanation for the creation of these vortices is still pending. This work contributes to understanding their creation by isolating the effect of the helix force perturbation on a symmetric wake from the impact of blade-related features like tip-vortices, hub vortex, or wake swirl. For this purpose, we perform Particle Image Velocimetry (PIV) measurements of a porous disc (PD) model in a wind tunnel. The PD is modified to mimic the helix but does not inherit the blade-related features present in a wind turbine wake. We observe the formation of two counter-rotating vortices in the far wake that deform the wake cross-section into a kidney shape, analogous to the structures present in the wake when helix active wake control is applied to a wind turbine. A conceptual comparison of PD wake and wind turbine wake implies that the wake swirl present in the turbine wake causes asymmetric reactions in several characteristics of the vortex system to changes in the rotational direction of the helix perturbation. Consequently, the dynamic, non-uniform helix perturbation alone is sufficient to activate the governing mechanisms that enhance the wake recovery when using helix active wake control, while blade-related phenomena are not fundamental to the principal processes.

Wind turbines in farms face challenges such as reduced power output and increased loading when their rows align with the wind direction - a phenomenon known as the wake effect. To address this issue, dynamic induction control has been proposed, which involves dynamically adjusting the induction of upstream turbines to enhance the mixing of the wake with the free stream. As a continuation of this method, downstream turbines could potentially leverage the periodic structure in the upstream turbines' wake to improve power production further downstream by synchronizing their dynamic induction control actions. This study investigates the potential of such an approach using a three-turbine scaled setup in a wind tunnel. The findings reveal that synchronization not only improves wake mixing downstream but also results in a substantial power gain on the synchronizing turbine, suggesting potential for a synchronization controller.

Wind farm control can play a key role in reducing the negative impact of wakes on wind turbine power production. The helix approach is a recent innovation in the field of wind farm control, which employs individual blade pitch control to induce a helical velocity profile in a wind turbine wake. This forced meandering of the wake has turned out to be very effective for the recovery of the wake, increasing the power output of downstream turbines by a significant amount. This paper presents a wind tunnel study with two scaled wind turbine models of which the upstream turbine is operated with the helix approach. We used tomographic particle image velocimetry to study the dynamic behavior of the wake under the influence of the helix excitation. The measured flow fields confirm the wake recovery capabilities of the helix approach compared with normal operation. Additional emphasis is put on the effect of the helix approach on the breakdown of blade tip vortices, a process that plays an important role in re-energizing the wake. Measurements indicate that the breakdown of tip vortices and the resulting destabilization of the wake are enhanced significantly with the helix approach. Finally, turbine measurements show that the helix approach was able to increase the combined power for this particular two-turbine setup by as much as 15%.

Wind energy has emerged as a prominent alternative energy source, harvesting energy through turbines to contribute sustainably to the electricity grid. Effective control of these turbines is crucial for regulating power generation, with wind farm control strategies geared toward maximizing on-demand energy generation. In this work, we propose a wind turbine regulator based on blade-pitch actuation and assess the impact of adopted turbine derating strategies on aerodynamic loading and downstream power availability in an experimental setting. By considering a derating strategy based on generator torque control law, we explore two wind farm control approaches: thrust balance and power compensation. Our findings highlight the advantages of balancing aerodynamic loads across the farm, preventing turbine saturation, and enhancing power availability by 3%-5% compared to a uniform power dispatch. Furthermore, the inclusion of power compensation results in a heightened upper limit in wind farm power tracking, indicating a 22% boost in wind farm power availability. This research underscores the potential benefits of innovative turbine regulation strategies for optimizing wind farm performance and enhancing overall energy flexibility.

Induction control methods offer a potential solution to minimizing wake effects that occur in large wind farms. This paper presents an experimental study on multiple induction control methods for wind farm power maximization. Wind tunnel experiments were conducted on two aligned scaled wind turbines. The upstream turbine was operated with static induction control, periodic dynamic induction control with collective pitch actuation, and dynamic individual pitch control (the helix approach). All wind farm control implementations were compared to a baseline case, which optimized the individual power extraction of both turbines. Tomographic particle image velocimetry was used to measure the wake of the upstream turbine. Based on turbine measurements, grid searches were employed to discover the optimal frequency and amplitude of the pitch actuation in the dynamic induction control cases. While static induction control showed increased wake velocities in the near wake, it did not provide an overall increase in power production of the two-turbine array. Dynamic induction control methods, especially the helix approach in the counterclockwise direction, were seen to significantly increase the total power output compared to the baseline control case. However, this improvement came with a larger amount of pitch actuation and increased fatigue loading of structural components in the fore-aft direction.

Operational modal analysis (OMA) is an essential tool for understanding the structural dynamics of offshore wind turbines (OWTs). However, the classical OMA algorithms require the excitation of the structure to be stationary white noise, which is often not the case for operational OWTs due to the presence of periodic excitation caused by rotor rotation. To address this issue, several solutions have been proposed in the literature, including the Kalman filter-based stochastic subspace identification (KF-SSI) method which eliminates harmonics through estimation and orthogonal projection. In this paper, an enhanced version of the KF-SSI method is presented that involves a concatenation step, allowing multiple datasets with similar environmental conditions to be used in the identification process, resulting in higher precision. This enhanced framework is applied to an operational OWT and compared to other OMA methods, such as the modified least-squares complex exponential and PolyMAX. Using field data from a multi-megawatt operational OWT, it is shown that the enhanced framework is able to accurately distinguish the first three bending modes with more stable estimates and lower variance compared to the original KF-SSI algorithm and follows a similar trend compared to other approaches.

Dynamic Induction Control (DIC) is a novel, exciting branch of Wind Farm Control. It makes use of time-varying control inputs to increase wake mixing, and consequently improve the velocity recovery rate of the flow and the power production of downstream turbines. The Pulse and the Helix are two promising DIC strategies that rely on sinusoidal excitations of the collective pitch and individual pitch of the blades, respectively. While their beneficial effects are evident in simulations and wind tunnel tests, we do not yet fully understand the physics behind them. We perform a systematic analysis of the dynamics of pulsed and helicoidal wakes by applying a data-driven approach to the analysis of data coming from Large Eddy Simulations (LES). Specifically, Dynamic Mode Decomposition (DMD) is used to extract coherent patterns from high-dimensional flow data. The periodicity of the excitation is exploited by adding a novel physics informed step to the algorithm. We then analyze the power spectral density of the resulting DMD modes as a function of the Strouhal number for different pitch excitation frequencies and amplitudes. Finally, we show the evolution in time and space of the dominant modes and comment on the recognizable patterns. By focusing on the modes that contribute the most to the flow dynamics, we gather insight on what causes the increased wake recovery rate in DIC techniques. This knowledge can then be used for the optimization of the signal parameters in complex layouts and conditions.

In the search for a lower levelized cost of wind energy, one approach is to increase the accuracy of wind turbine measurements such as wind speed and wind direction. The sensors available on wind turbines are susceptible to local turbulence and measurement bias, which can result in suboptimal turbine performance. As an alternative, recent research has considered using the sensor measurements in a coordinated manner. With such a cooperative approach, the local wind conditions can be estimated more accurately and reliably without the need for additional measurement equipment. In this paper, a novel wind field estimation approach is presented that estimates the local wind conditions based on turbine measurements using Gaussian processes. We show that the estimation framework is able to improve the accuracy of the wind direction estimate both in an offline and online manner, as well as identify possible biases in the sensors and reduce unnecessary wind turbine yaw activity.

In recent years, wake steering has been established as a promising method to increase the energy yield of a wind farm. Current practice in estimating the benefit of wake steering on the annual energy production (AEP) consists of evaluating the wind farm with simplified surrogate models, casting a large uncertainty on the estimated benefit. This paper presents a framework for determining the benefit of wake steering on the AEP, incorporating simulation results from a surrogate model and large eddy simulations in order to reduce the uncertainty. Furthermore, a time-varying wind direction is considered for a better representation of the ambient conditions at the real wind farm site. Gaussian process regression is used to combine the two data sets into a single improved model of the energy gain. This model estimates a 0.60% gain in AEP for the considered wind farm, which is a 76% increase compared to the estimate of the surrogate model.

Floating offshore wind turbines allow wind energy to be harvested in deep waters. However, additional dynamics and structural loads may result when the floating platform is being excited by wind and waves. In this work, the conventional wind turbine controller is complemented with a novel linear feedforward controller based on wave measurements. The objective of the feedforward controller is to attenuate rotor speed variations caused by wave forcing. To design this controller, a linear model is developed that describes the system response to incident waves. The performance of the feedback-feedforward controller is assessed by a high-fidelity numerical tool using the DTU 10MW turbine and the INNWIND.EU TripleSpar platform as references. Simulations in the presence of irregular waves and turbulent wind show that the feedforward controller effectively compensates the wave-induced rotor oscillations. The novel controller is able to reduce the rotor speed variance by 26%. As a result, the remaining rotor speed variance is only 4% higher compared to operation in still water.