Wind Farm Control Research Articles

Multi-model approach for wind resource assessment

This study presents a multi-model approach for wind resource assessment of a wind farm affected by external wakes. The Weather Research and Forecasting model (WRF), a mesoscale model, is employed to simulate external wind farm wakes, while the Farm Optimization and eXtended yield Evaluation Software (FOXES), an engineering model, is used to simulate the wind farm of interest. This hybrid approach addresses the limitation of both models, mainly the lack of layout effects in mesoscale models and the poor representation of cluster wakes in engineering models. A case study, focusing on the Kaskasi wind farm in the Heligoland cluster, shows that the WRF model predicts larger wake losses compared to FOXES, with the multi-model approach yielding intermediate results. Systematic differences are found as a function of wind speed and seasonality, while the models behave differently as a function of turbulence intensity. The external wake effect was clearly identified for one wind direction sector in WRF and the multi-model approach, while FOXES failed to represent this. The proposed methodology does not only enhance classic resource assessment, but also facilitates efficient layout optimization using cluster waked inflow and allows for wind farm control studies, contributing to both planning and operational phases of wind farm management.

LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment

This work presents the validation of an extended version of the control-oriented, dynamic wind farm flow solver SPLINTER. The two-dimensional model is applied to use cases of wake steering by yaw misalignment and inflow wind direction variations and the results are compared to large-eddy simulations (LES). While SPLINTER is able to reproduce the antagonal behaviour of decreasing upstream and increasing downstream turbine power under wake deflection, a systematic deviation of the downstream power is detected and quantified, which is connected to underrepresented three-dimensional wake effects. In case of changing inflow wind direction, SPLINTER is capable of computing movement and shape of the bending wakes. The model smooths small-scale turbulent structures and disturbances and does not reproduce wake meandering, but manages to describe the evolution of the mean flow, which is tested by averaging over an ensemble of LES and comparing the resulting flow fields and turbine power time series. Under dynamic inflow conditions, SPLINTER is able to predict at which time intervals and at which rates downstream turbines will be influenced by wakes, which can improve the accuracy of short-term power and load forecasting and enables its application to online model predictive wind farm control.

Experimental comparison of induction control methods for wind farm power maximization on a scaled two-turbine setup

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.

Wind tunnel investigations of an individual pitch control strategy for wind farm power optimization

Abstract. This article presents the results of an experimental wind tunnel study which investigates a new control strategy named Helix. The Helix control employs individual pitch control for sinusoidally varying yaw and tilt moments to induce an additional rotational component in the wake, aiming to enhance wake mixing. The experiments are conducted in a closed-loop wind tunnel under low-turbulence conditions to emphasize wake effects. Highly sensorized model wind turbines with control capabilities similar to full-scale machines are employed in a two-turbine setup to assess wake recovery potential and explore loads on both upstream and downstream turbines. In a single-turbine study, detailed wake measurements are carried out using a fast-response five-hole pressure probe. The results demonstrate a significant improvement in energy content within the wake, with distinct peaks for clockwise and counterclockwise movements at Strouhal numbers of approximately 0.47. Both upstream and downstream turbine dynamic equivalent loads increase when applying the Helix control. The time-averaged wake flow streamwise velocity and rms value reveal a faster wake recovery for actuated cases in comparison to the baseline. Phase-locked results with azimuthal position display a leapfrogging behavior in the baseline case in contrast to the actuated cases, where distorted shedding structures in the longitudinal direction are observed due to a changed thrust coefficient and an accompanying lateral vortex shedding location. Additionally, phase-locked results with the additional frequency reveal a tip vortex meandering, which enhances faster wake recovery. Comparing the Helix cases with clockwise and counterclockwise rotations, the latter exhibits slightly higher gains and faster wake recovery. This difference is attributed to Helix' additional rotational component acting in either the same or the opposite direction as the wake rotation. Overall, both Helix cases exhibit significantly faster wake recovery compared to the baseline, indicating the potential of this technique for improved wind farm control.

Combining wake redirection and derating strategies in a load-constrained wind farm power maximization

Abstract. Power derating and wake redirection are two wind farm control techniques proposed in the last decade as means for increasing the overall wind farm power output. While derating operations are associated with a limited gain in terms of farm energy harvesting and with a decrease in turbine loading levels, farm controls based on wake redirection proved, both in silico and experimental tests, to entail significant increases in the overall wind farm power output. However, according to wake redirection strategies, the upstream wind turbines may typically operate at large yaw misalignment angles, and the possible increase in loads that the machines may experience in such conditions represents a source of concern when it comes to testing this control on existing farms that are not specifically designed for prolonged misaligned operations. In this work, it is first demonstrated that a suitable derating level can compensate for the increase in the rotor loads associated with large misalignment angles. Secondarily, two load-constrained wind farm controls based on a combination of wake redirection and derating are proposed with the aim of maximizing the overall farm output while maintaining unaltered design load envelope of the wind turbines operating within the controlled wind farm.

A new analytical wind turbine wake model considering the effects of coriolis force and yawed conditions

Wind turbine wakes significantly affect power production and impose higher loads on downstream turbines. Therefore, the development of accurate and efficient wake models is important for optimizing wind farm layouts and predicting wind turbine performance. This study introduces a novel analytical wake model for yawed wind turbines that incorporates the effects of the Coriolis force. The wake deflection in the far wake region is derived through the application of the principles of mass and momentum conservation. In the near wake, the deflection is assumed to be linear with distance. A Gaussian distribution is assumed for the velocity deficit within the wind turbine wake. Two approaches have been proposed to estimate the onset of the far wake region. While the first approach employs a simplified empirical formula, the second approach utilizes an iteration-based method. The proposed analytical wake model has been validated against computational fluid dynamics (CFD) results. Subsequently, the effects of several important parameters on the wake deflection have been systematically investigated. Overall, the simulation results showed a satisfactory agreement between the CFD results and those obtained from the proposed model. Furthermore, the study concluded that the Coriolis force can exert significant effects on wake deflection, particularly in the far wake region, confirming previous findings from numerical simulations. Due to its simplicity and computational efficiency, the proposed model can be readily used in several applications, including wind farm layout optimization, control and risk assessment.

Modelling of linear large-capacity wind turbines with application to distributed control in wind farms

ABSTRACT Due to the widespread penetration of wind energy, large-capacity wind turbines (WTs) are becoming more conspicuous. In this paper, a linear control-oriented large-capacity wind turbine (WT) model is redesigned based on the 15 MW WT in openFAST. Compared with the original model, the redesigned model is more efficient and conducive to the design of linear controllers. To verify the validity of the redesigned WT model, we design a distributed control scheme with deloading operation of the wind farm (WF) to fairly share the load. Simulations are performed to verify the effectiveness of the application in the WF.

Centralized Control of Large-Scale Wind Farm for System Frequency Stabilization of the Power System

Centralized Control of Large-Scale Wind Farm for System Frequency Stabilization of the Power System

Reinforcement Learning-Based Multiobjective Control of Grid-Connected Wind Farms

Reinforcement Learning-Based Multiobjective Control of Grid-Connected Wind Farms

Analyzing the Effects of Atmospheric Turbulent Fluctuations on the Wake Structure of Wind Turbines and Their Blade Vibrational Dynamics

In recent trends, a rising demand for renewable energy has driven wind turbines to larger proportions, where lighter blade designs are often adopted to reduce the costs associated with logistics and production. This causes modern utility-scale wind turbine blades to be inherently more flexible, and their amplified aeroelastic sensitivity results in complex multi-physics reactions to variant atmospheric conditions, including dynamic patterns of aerodynamic loading at the rotor and vortex structure evolutions within the wake. In this paper, we analyze the influence of inflow variance for wind turbines with large, flexible rotors through simulations of the National Rotor Testbed (NRT) turbine, located at Sandia National Labs’ Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas. The Common Ordinary Differential Equation Framework (CODEF) modeling suite is used to simulate wind turbine aeroelastic oscillatory behavior and wind farm vortex wake interactions for a range of flexible NRT blade variations, operating in differing conditions of variant atmospheric flow. CODEF solutions of turbine operation in Steady-In-The-Average (SITA) wind conditions are compared to SITA wind conditions featuring a controlled gust-like pulse overimposed, to isolate the effects of typical wind fluctuations. Finally, simulations of realistic time-varying wind conditions from SWiFT meteorological tower measurements are compared to the solutions of SITA wind conditions. These increasingly complex atmospheric inflow variations are tested to show the differing effects evoked by various patterns of spatiotemporal atmospheric flow fluctuations. An analysis is presented for solutions of wind turbine aeroelastic response and vortex wake evolution, to elucidate the consequences of variant inflow, which pertain to wind turbine dynamics at an individual and farm-collective scale. The comparisons of simulated farm flow for SITA and measured fluctuating wind conditions show that certain regions of the wake contain up to a 12% difference in normalized axial velocity, due to the introduction of wind fluctuations. The findings of this study prove valuable for practical applications in wind farm control and optimization strategies, with particular significance for modern utility-scale wind power plants operating in variant atmospheric conditions.

Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities

As the maturity and complexity of wind energy systems increase, the operation of wind turbines in wind farms needs to be adjustable in order to provide flexibility to the grid operators and optimize operations through wind farm control. An important aspect of this is monitoring and managing the structural reliability of the wind turbines in terms of fatigue loading. Additionally, in order to perform optimization, uncertainty analyses, condition monitoring, and other tasks, fast and accurate models of the turbine response are required. To address these challenges, we present the controller tuning and surrogate modeling for a wind turbine that is able to vary its power level in both down-regulation and power-boosting modes, as well as reducing loads with an individual blade control loop. Two methods to derive the setpoints for down-regulation are discussed and implemented. The response of the turbine, in terms of loads, power, and other metrics, for relevant operating conditions and for all control modes is captured by a data-driven surrogate model based on aeroelastic simulations following two regression approaches: a spline-based interpolation and a Gaussian process regression model. The uncertainty of the surrogate models is quantified, showing a good agreement with the simulation with a mean absolute error lower than 4% for all quantities considered. Based on the surrogate model, the aeroelastic response of the entire wind turbine for the different control modes and their combination is analyzed to shed light on the implications of the control strategies on the fatigue loading of the various components.

A new 3D asymmetric double-Gaussian wake analytical model for horizontal-axis wind turbines

A new three-dimensional analytical model named 3DADG that is able to describe asymmetric evolution of wind turbine wake is proposed in this paper. Due to the combined effects of wind wheel rotation and wind shear effects wake attenuation shows asymmetric properties. However, existing wake analytical models fail to describe such unique properties. To solve this problem, spatial dependencies of the wake expansion rate and deficit coefficient are revealed to successfully present horizontal attenuations of the wake varying from asymmetric-double-Gaussian in the near and transition wake regions, and then to single-Gaussian in the far wake region. The wind shear effect is also considered in the model to include asymmetric vertical wake speed attenuation. This model is derived from the laws of mass and momentum conservation. It is verified through comparisons with the results of wind tunnel “blind test” and LES simulation. The model proposed is capable to show spatial evolution of wind turbine wake from a three-dimensional perspective. Sensitivities of thrust coefficient and turbulence intensity to the model are also investigated. It not only promotes development of wake analytical model but also provides a useful tool for power prediction and wake control of wind farms under wake effects.

Optimization Method of Multi-Mode Model Predictive Control for Wind Farm Reactive Power

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme.

An Online Feedback-Based Local Method for Topology Identification and Var/Volt Control in Radial Wind Farms

An Online Feedback-Based Local Method for Topology Identification and Var/Volt Control in Radial Wind Farms

A physics-guided machine learning framework for real-time dynamic wake prediction of wind turbines

Efficient and accurate prediction of the wind turbine dynamic wake is crucial for active wake control and load assessment in wind farms. This paper proposes a real-time dynamic wake prediction model for wind turbines based on a physics-guided neural network. The model can predict the instantaneous dynamic wake field under various operating conditions using only the inflow wind speed as input. The model utilizes Taylor's frozen-flow hypothesis and a steady-state wake model to convert instantaneous inflow wind speed and turbine parameters into neural network input features. A deep convolutional neural network then maps these features to desired wake field snapshots, enabling dynamic wake predictions for wind turbines. To train the model, we generated approximately 255 000 instantaneous flow field snapshots of single-turbine wakes using the large eddy simulation, covering different thrust coefficients and yaw angles. The model was trained using the supervised learning method and verified on the test set. The results indicate that the model can effectively predict the dynamic wake characteristics, including the dynamic wake meandering and the wake deflection of the yawed turbines. The model can also assess both the instantaneous wake velocity and the instantaneous wake center of a wind turbine. At a thrust coefficient of 0.75, the root mean square error for the predicted instantaneous wake velocity is around 6.53%, while the Pearson correlation coefficient for the predicted instantaneous wake center can reach 0.624. Furthermore, once the model is trained, its prediction accuracy does not decrease with the increase in the time span.

Maximizing wind farm power output with the helix approach: Experimental validation and wake analysis using tomographic particle image velocimetry

AbstractWind 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%.

Yaw misalignment in powertrain degradation modeling for wind farm control in curtailed conditions

A framework characterizing the degradation of wind turbines for use in multiple-input damage-aware farm control is suggested. The focus is on the fatigue damage of the powertrain (drivetrain + generator) system, but the methodology may be extended to other components. A database of steady-state damage analyses for different operating conditions (average wind speeds, turbulence levels, power demands, and yaw misalignment angles) using aero-hydro-servo-elastic simulations is first generated. Then, a weighted damage index based on probabilistic long-term fatigue damage analysis of the powertrain system components is suggested and used to represent degradation at the farm level for control purposes. The focus is on curtailed conditions where the farm controller dispatches power commands to individual turbines in order to track a demanded power reference (rather than seeking to maximize power) at the farm level. As a secondary objective, the controller seeks to mitigate degradation through a smart combination of power commands and yaw offset angles, making use of the weighted degradation index. The potential of the proposed approach is demonstrated through a case study on the TotalControl Reference Wind Power Plant in a FLORIS-based simulation framework. The proposed farm controller is compared with the conventional one without damage mitigation feature and with damage mitigation but without yaw angle as the control input. It is found that combining yawing and downregulation effectively slows down degradation on the main bearing and powertrain as a whole.

Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data

Real-time acquisition of dynamic wake field information has garnered substantial attention in the wind farm industry, as it provides a crucial data source for intelligent wind farm monitoring and control. However, the existing wind measurement technologies, such as light detection and ranging (LiDAR), are limited to sparse data point measurements. This paper explores the utilization of a Physics-Informed Neural Network (PINN) to reconstruct wind turbine wake dynamics, specifically focusing on the influence of active wind turbine yaw operation on wake evolution. The methodology involves creating a tailored loss function that combines sparse wake measurement data with the Navier-Stokes (NS) equations. More precisely, the neural network incorporates the NS equations as constraints to guide the prediction of physical quantities in the output, including downwind velocity, crosswind velocity, and pressure. Taking the dynamic wake during yawing as a case study, the proposed method showcases remarkable universality and robustness across diverse scenarios involving varying scanning angle intervals, measurement point spacings, frequencies, and noise levels. It successfully captures the dynamic trends in wake evolution during yawing and accurately forecasts the wake trajectory and deflection. Even when tested with actual wake measurement data, the method can still effectively reconstruct the flow field, indicating significant potential for the real wind farm yaw control.

Towards real-time optimal control of wind farms using large-eddy simulations

Abstract. Large-eddy simulations (LESs) are commonly considered too slow to serve as a practical wind farm control model. Using coarser grid resolutions, this study examines the feasibility of LES for real-time, receding-horizon control to optimize the overall energy extraction in wind farms. By varying the receding-horizon parameters (i.e. the optimization horizon and control update time) and spatiotemporal resolution of the LES control models, we investigate the trade-off between computational speed and controller performance. The methodology is validated on the TotalControl Reference Wind Power Plant using a fine-grid LES model as a wind farm emulator. Analysis of the resulting power gains reveals that the performance of the controllers is primarily determined by the receding-horizon parameters, whereas the grid resolution has minor impact on the overall power extraction. By leveraging these insights, we achieve near-parity between our LES-based controller and real-time computational speed, while still maintaining competitive power gains up to 40 %.

A grouping strategy for reinforcement learning-based collective yaw control of wind farms

Reinforcement learning (RL) algorithms are expected to become the next generation of wind farm control methods. However, as wind farms continue to grow in size, the computational complexity of collective wind farm control will exponentially increase with the growth of action and state spaces, limiting its potential in practical applications. In this Letter, we employ a RL-based wind farm control approach with multi-agent deep deterministic policy gradient to optimize the yaw manoeuvre of grouped wind turbines in wind farms. To reduce the computational complexity, the turbines in the wind farm are grouped according to the strength of the wake interaction. Meanwhile, to improve the control efficiency, each subgroup is treated as a whole and controlled by a single agent. Optimized results show that the proposed method can not only increase the power production of the wind farm but also significantly improve the control efficiency.