Design and analysis of a wake steering controller with wind direction variability

Wake losses from neighboring plants may become a major factor in wind plant design and control as additional plants are constructed in areas with high wind resource availability. Because plant wakes span a large range of physical scales, from turbine rotor diameter to tens of kilometers, it is unclear whether conventional wake models or turbine control strategies are effective at the plant scale. Wake steering and axial induction control are evaluated in the current work as means of reducing the impact of neighboring wind plants on power and levelized cost of electricity. FLOw Redirection and Induction in Steady State (FLORIS) simulations were performed with the Gauss–Curl Hybrid and TurbOPark wake models as well as two operation and maintenance models to investigate control setpoint sensitivity to wake representation and economic factors. Both wake models estimate losses across a range of atmospheric conditions, although the wake loss magnitude is dependent on the wake model. Annual energy production and levelized cost of electricity are driven by wind direction frequency, with frequently aligned plants experiencing the greatest losses. However, both wake steering and axial induction are unable to mitigate the impact of upstream plants. Wake steering is constrained by plant geometry, since wake displacement is much less than the plant wake width, while axial induction requires curtailing the majority of turbines in upstream plants. Individual turbine strategies are limited by their effective scale and model representation. New wake models that include plant-scale physics are needed to facilitate the design of effective plant wake control strategies.

The accuracy of analytical wake models applied in wind farm layout optimization (WFLO) problems plays a vital role in the present era that the high-fidelity methods such as LES and RANS are still not able to handle an optimization problem for large wind farms. Based on a verity of analytical wake models developed in the past decades, FLOw Redirection and Induction in Steady State (FLORIS) has been published as a tool integrated several widely used wake models and the expansions for them. This paper compares four wake models selected from FLORIS by applying three classical WFLO scenarios. The results illustrate that the Jensen wake model is the fastest one but the defect of underestimation of velocity deficit is obvious. The Multi Zone model needs to be applied additional tunning on the parameters inside the model to fit specific wind turbines. The Gaussian-Curl wake model as an advanced expansion of the Gaussian wake model does not perform an observable improvement in the current study that the yaw control is not included. The default Gaussian wake model is recommended to be used in the WFLO projects which implemented under the FLROIS framework and has similar wind conditions with the present work.

A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow

Abstract. Wind-farm control strategies aim to increase the efficiency, and therefore lower the levelized cost of energy, of wind farms. This is done by using turbine settings such as the yaw angle, blade pitch angles, or generator torque to manipulate the wake that negatively affects downstream turbines in the farm. Two inherently different wind-farm control methods have been identified in the literature: wake steering (WS) and active wake mixing (AWM). As one of two companion papers focused on understanding practical aspects of these two wind-farm control strategies using large-eddy simulation (LES), we below analyze the wake quantities of interest for a single wind turbine performing WS and AWM, while the companion article (Frederik et al., 2025) focuses on turbine quantities of interest including power and structural loads for the same computational setup and also includes two-turbine arrays with full and partial wake overlap. The simulations, which are based in the LES solver AMR-Wind, are tailored to have inflow conditions representative of measurements from a site off the East Coast of the US, including with strong veer and low turbulence. The turbine, which is modeled in OpenFAST and coupled to the LES, is the IEA 15 MW, an open-source offshore design. After presenting an overview of the wake recovery for the different wake-control cases, the analysis probes the fluid-dynamic causes for the different performance of the arrays reported in the companion article by examining control volumes around the wakes and the budget of the mean-flow kinetic energy (MKE) within these volumes. In the high veer environment considered, the MKE recovery is dominated by mean convection, and this is shown to especially benefit the WS strategy when a neighboring turbine is directly downstream: there is ≈65 % more available power for a downstream turbine than in the baseline case, and this power is gained primarily through mean convection on the left-tip and top-tip faces of the control volume. However, the case with imperfect knowledge of the exact wind direction favors the pulse-type AWM strategy, largely because of ≈9 % increased turbulent entrainment from aloft versus the baseline that could be related to an apparent resistance to skewing in the pulsed wake. The general reduced effectiveness of helix-type and other individual-pitch-based AWM strategies for inflow with high veer and low turbulence as reported in the companion paper is due, in part, to low magnitudes of phase-averaged turbulent entrainment. Two main findings of this study are thus that veer has a significant impact on the effectiveness of different wake-control strategies and that pulse-type AWM may be a useful strategy when the objective is power maximization in realistic, offshore flow environments with imperfect knowledge of the exact wake overlap position on the downstream turbine.

Sensitivity of air change rates in a naturally ventilated atrium space subject to variations in external wind speed and direction

The wake steering control in wind farms has gained significant attention in the last years. This control strategy has shown promise to reduce energy losses due to wake effects and increase the energy production in a wind farm. However, wind conditions are variable in wind farms, and the measurements are uncertain what should be considered in the design of wake steering control strategies. This paper proposes using the probabilistic learning on manifold (PLoM), which can be viewed as a supervised machine learning method, to enable the wake steering optimization under uncertainty. The expected power generation is estimated considering uncertainties in wind speed and direction with good accuracy and reduced computational cost for two wind farm layouts, which expand the application of machine learning models in wake steering. Furthermore, the analysis shows the potential gain with the application of wake steering control.

Ideally, optimum power for a single turbine is obtained when the wind-turbine is aligned with the wind direction. However in multi-turbine wind-farm set-up, wake effects lead to decreased power production from downstream turbine [1, 2, 3, 4, 5]. Hence, a control strategy based on wake steering involves misalignment of upstream turbines with the wind direction causing their wakes to deflect away from downstream wind turbines needs to be investigated. A great deal of work has been put into dynamically controlling the orientation of the individual wind turbines to maximize the power output of the farm [6, 7, 8, 9]. In the wake-steering based control, the misaligned wind turbines produce less power, while the performance of downstream turbines gets enhanced which increases overall net power gain for the wind power plant. Traditionally, the benefits of wake steering have been demonstrated assuming fixed wind directions (e.g., using high-fidelity modeling). Amongst the most recent techniques, particularly promising is the use of Reinforcement learning (RL), which is a branch of machine learning where models are trained to make decisions based on observations of their environment. It is a flexible framework for devising strategies for solving optimal control problems in a broad range of applications across the sciences. Early attempts at using Reinforcement learning for wake steering have been carried out [7, 8, 9], and show promising results. In practice, however, wake-steering controllers must operate in dynamic wind environments in which the wind conditions are estimated from imperfect measurements. Hence, a reinforcement learning framework is developed in this work for dynamic wind conditions. The results show that the framework is promising, and we compare the deep reinforcement learning approach against a considerably more expensive traditional optimization approach which serves as a good baseline. Future work could include looking at more realistic wake models, steering in the presence of noisy observations, and incorporating weather predictions.

This paper describes an estimation algorithm for velocity and power output signals in a wind farm under changing wind direction. A graph-theoretic definition describes the wind farm as a collection of nodes (turbines) and time-varying weighted edges (inter-turbine wake propagation) that change as a function of incoming wind direction. The velocity at each turbine is determined through a discrete input-output model. Changes in wind direction serve as the input and the output is defined in terms of a time-varying weighted adjacency matrix that depends on the time-delay of information propagation between turbines. These delays, which are defined in terms of the advection speed of the wind and the distance between the turbines, capture the delayed effect of wind direction changes on the inter-connectivity of the graph as the wind conditions at the farm inlet propagate through the turbine array. An event-based update framework is employed to capture time-dependent topology changes due to shifts in wind direction. Simulation results for dynamically changing wind inlet directions to a circular wind farm are compared to predictions from both the static and dynamic versions of the FLOw Redirection and Induction in Steady State (FLORIS) model. The approach is shown to enable real-time tracking of dynamic changes to wind farm power output within a framework that can be easily integrated into real-time, horizon-based, control strategies that typically do not account for wind direction changes.

Variations in wind velocity and direction due to the mounting structure are studied using Gill anemometers mounted on 1.8 m long arms of a 232 m high tower. The tower «shadowing» effect for the observed range, from 0 to 20 m/s, is in linear correlation with the velocity. The polar diagrams of the variations in wind speed and direction are presented as a function of the wind's direction. The horizontal component decreases by 15% when the anemometer is on the windward side of the tower, and by a maximum of 80% on the lee side. In the latter case the decrease is highly sensitive to wind direction. Variations in direction do not exceed 15 degrees.

Surface mesoscale structures and wind variability in the northern Gulf of California (NGC) were analyzed using data from SeaWiFS (1 km) and QuikSCAT (1 day). The months of February and March were selected as representative of winter and the months of August and September were selected as representative of summer. Chlorophyll‐a (Chla) and wind time series were constructed using 16 points to analyze temporal variability and wind magnitude‐Chla temporal and spatial linear correlation. Wind components along and across the NGC were found to play an equally significant role regarding wind variability, contrary to what is currently recognized. Wind magnitude tended to be stronger in winter and had less wind direction variability than in summer. A bimodal wind pattern was registered for each season, each wind direction explaining less than 30% of total variability. The zone with minimum variability in wind direction and maximum variability in wind magnitude was located northwest of the NGC, the same area where wind magnitude‐Chla correlation was significant (p < 0.05). Winter and summer surface circulation patterns are more complex than currently documented. Summer is characterized by numerous plumes and eddies (especially anticyclonic), some traveling from coast to coast, enhancing the exchange of suspended material. Winter varied greatly interannualy, also showing many plumes and eddies (especially cyclonic), but was very heterogeneous, opposite to summer. Significant summer correlation was mostly positive, whereas in winter the significant correlation was equally positive and negative, maybe as a consequence of higher nutrient availability during this season.

The magnitude of wake interactions between individual wind turbines depends on the atmospheric stability. We investigate strategies for wake loss mitigation through the use of closed-loop wake steering using large eddy simulations of the diurnal cycle, where variations in the surface heat flux in time modify the atmospheric stability, wind speed and direction, shear, turbulence, and other atmospheric boundary layer flow (ABL) features. The closed-loop wake steering control methodology developed in Part 1 (Howland et al., Wind Energy Science, 2020, 5, 1315–1338) is implemented in an eight turbine wind farm in large eddy simulations of the diurnal cycle. The optimal yaw misalignment set-points depend on the wind direction, which varies in time during the diurnal cycle. To improve the application of wake steering control in transient ABL conditions with an evolving mean flow state, we develop a regression-based wind direction forecast method. We compare the closed-loop wake steering control methodology to baseline yaw aligned control and open-loop lookup table control for various selections of the yaw misalignment set-point update frequency, which dictates the balance between wind direction tracking and yaw activity. Closed-loop wake steering with set-point optimization under uncertainty results in higher collective energy production than both baseline yaw aligned control and open-loop lookup table control. The increase in wind farm energy production for closed- and open-loop wake steering control compared to baseline yaw aligned control, is 4.0–4.1 % and 3.4–3.8 %, respectively, with the range indicating variations in the energy increase results depending on the set-point update frequency. The primary energy increases through wake steering occur during stable ABL conditions. Open-loop lookup table control decreases energy production in the convective ABL conditions simulated, compared to baseline yaw aligned control, while closed-loop control increases energy production in convective conditions.

<strong class="journal-contentHeaderColor">Abstract.</strong> The magnitude of wake interactions between individual wind turbines depends on the atmospheric stability. We investigate strategies for wake loss mitigation through the use of closed-loop wake steering using large eddy simulations of the diurnal cycle, in which variations in the surface heat flux in time modify the atmospheric stability, wind speed and direction, shear, turbulence, and other atmospheric boundary layer (ABL) flow features. The closed-loop wake steering control methodology developed in Part 1 (<span class="cit" id="xref_altparen.1"><a href="#bib1.bibx33">Howland et al.</a>, <a href="#bib1.bibx33">2020</a><a href="#bib1.bibx33">c</a></span>, <a href="https://doi.org/10.5194/wes-5-1315-2020">https://doi.org/10.5194/wes-5-1315-2020</a>) is implemented in an example eight turbine wind farm in large eddy simulations of the diurnal cycle. The optimal yaw misalignment set points depend on the wind direction, which varies in time during the diurnal cycle. To improve the application of wake steering control in transient ABL conditions with an evolving mean flow state, we develop a regression-based wind direction forecast method. We compare the closed-loop wake steering control methodology to baseline yaw-aligned control and open-loop lookup table control for various selections of the yaw misalignment set-point update frequency, which dictates the balance between wind direction tracking and yaw activity. In our diurnal cycle simulations of a representative wind farm geometry, closed-loop wake steering with set-point optimization under uncertainty results in higher collective energy production than both baseline yaw-aligned control and open-loop lookup table control. The increase in energy production for the simulated wind farm design for closed- and open-loop wake steering control, compared to baseline yaw-aligned control, is 4.0 %–4.1 % and 3.4 %–3.8 %, respectively, with the range indicating variations in the energy increase results depending on the set-point update frequency. The primary energy increases through wake steering occur during stable ABL conditions in our present diurnal cycle simulations. Open-loop lookup table control decreases energy production in the example wind farm in the convective ABL conditions simulated, compared to baseline yaw-aligned control, while closed-loop control increases energy production in the convective conditions simulated.

Most of the fire accidents of exterior insulation in recent years started with the ignition of unprotected and exposed insulation materials. The fire spread on the thermal insulation layer will be affected by outdoor wind. In this paper, the influence of wind direction and wind speed on wall fire propagation characteristics was studied by carrying out medium sized experiments. The temperatures on the wall surface, radiation heat flux nearby, the infrared image and flame image were recorded during the tests with variation of wind direction and wind speed. It is found that with the increase of the angle between the wind direction and the wall, the surface temperature of the material, the radiation heat flux released decreases.This study results can provide valuable references for optimizing the fire safety design of exterior insulation layer.

Quantifying the impacts on turbine loads during wind farm control is an important consideration in assessing power production benefits. Wake steering controls aim to improve total wind farm performance by coordinating the control actions of individual turbines, wherein an upstream turbine is intentionally yawed at an offset angle from the measured wind direction. Consequently, this redirects its wake for improved power production and potentially reduces fatigue loads of the downstream turbines. This paper studies the lifetime fatigue loads associated with wake steering by using utility-scale wind turbine experimental data to conduct an analysis on a pair of wind turbines. This study was part of a large field experiment in which a group of five GE 1.5-MW SLE CWE turbines were selected as targets for conducting wake steering research. A standard loads instrumentation package and data acquisition system were installed on two turbines within the cluster to measure turbine fundamental loads. The time-series databases were used to calculate loads statistics as well as short-term and lifetime damage equivalent loads. Fatigue calculations followed the guidance in Annex H of the International Electrotechnical Commission standard 61400-1, Edition 4. Lifetime fatigue calculation results are presented in this analysis; three methods of assessing lifetime fatigue were used to determine percent difference for blade root moments, main shaft moments, main shaft torque, and tower top torque. For all three fatigue treatments, some components’ lifetime fatigue increases for the controlled turbine; however, the downwind turbine experienced a reduction in lifetime fatigue and combined effect for the turbine pair results in a reduction of fatigue when wake steering controls are applied.

A shelter belt study—Relative shelter, effective winds and maximum efficiency