Aeroacoustic Assessment of Wind Plant Controls

Wind plant control strategies are used more and more to mitigate wake losses within wind plants. The most commonly implemented strategy for wake loss mitigation is that of wake steering---introducing lateral deflections to the momentum-deficit characterizing the wake by intentionally yawing a wind turbine with respect to the incoming wind direction. Wake steering has been shown experimentally and computationally to increase annual energy production (AEP) of a wind plant by 1-2% reliably, with the potential for much greater gains under conditions where wake losses are most pronounced. As wake steer becomes a common and accepted operational strategy for large wind plants, external effects must be quantified to ensure that the strategy remains viable moving forward.

External or secondary effects of wake steering include the potential for increased structural loads, which could increase operations and maintenance costs, and increased aeroacoustic noise generation, which could impose additional operational constraints. To meet local constraints on noise generation, wind turbines may be operated at a reduced tip speed, impacting the mechanical efficiency of the machine and the levelized cost of energy (LCOE). For wake steering to be widely applied as a strategy to mitigate wake losses, changes in the aeroacoustic noise emissions of utility scale wind turbines under yawed operation must be quantified.

All work in the Aeroacoustic Assessment of Wind Plant Controls project was conducted by researchers from the National Renewable Energy Laboratory at the Flatirons Campus. Aeroacoustic data was collected behind the DOE-owned GE 1.5 MW SLE wind turbine.

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The American WAKE ExperimeNt (AWAKEN)

The American WAKE ExperimeNt (AWAKEN) is a landmark collaborative international wake observation and validation campaign. Wake interactions are among the least understood and most impactful physical interactions in wind plants today, leading to unexpected power losses and increased operations and maintenance costs.

The AWAKEN campaign is designed to gather observational data to address the most pressing science questions about wind turbine wake interactions and aerodynamics and to further understand wake behavior and validate wind plant models.

Simultaneously, the AWAKEN campaign will also focus on testing of wind farm control strategies that have been shown to increase wind plant power production. Leveraging the expertise and resources of a large body of National Laboratories, academic institutions, and industry partners will lead to improved wind farm layout with greater power production and improved reliability, ultimately leading to lower wind energy costs.

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Offshore Wind Energy - Buoy Lidar Project

A 2014 study estimated that U.S. offshore wind energy could generate enough power for 17 million homes. More than a dozen offshore wind farms are in various stages of development in the United States. The demonstration projects in Virginia and New Jersey receive funding from the U.S. Department of Energy (DOE).

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Coastal Wind Profiler

Wind is a variable energy resource. As its proportional contribution to overall electrical power increases, accurate forecasting of wind power production becomes increasingly important to maintain stability in the electrical grid and low power costs. In turn, forecast accuracy depends substantially on data to provide accurate initialization of numerical weather forecast models. The U.S. Department of Energy (DOE), in collaboration with the National Oceanic and Atmospheric Administration (NOAA), recently completed installation of three new wind-profiling radars on the Washington and Oregon coasts to provide data for model initialization.

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Improving the Mapping and Prediction of Offshore Wind Resources

With few weather observations over the coastal Atlantic Ocean, atmospheric models are an essential bridge to map offshore wind resources and forecast wind power potential, up to hours or days in advance. Unfortunately, these models have large wind speed uncertainties over the ocean, especially at wind turbine height a few hundred feet above the ocean.

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Leading-edge Erosion Study (LEES) Project

Airfoil Performance Degradation due to Roughness and Leading-edge Erosion

Wind farms often underperform predicted power output by 10 to 30 percent relative to manufacturer predictions. A potential aerodynamic explanation is that blade roughness caused by insect impingement and leading-edge erosion decreases lift and drag as opposed to “clean” blades. These effects are difficult to test in the field because aerodynamic performance cannot be measured directly and can be affected by many factors that cannot be controlled in field experiments. This project provides aerodynamic performance data using wind tunnel measurements of representative inboard and outboard blade sections contaminated with various types and levels of roughness and leading-edge erosion. Results include aerodynamic load coefficients and measurements of laminar-to-turbulent transition location as functions of Reynolds number and angle of attack for various roughness configurations.

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Lake Michigan Wind Assessment

A study of wind speed and direction over Lake Michigan was conducted to determine if there was sufficient energy potential to justify further work toward wind farm development.

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Mesoscale-Microscale Coupling

This model performance study features several federally funded U.S. laboratories collaborating on various mesoscale and microscale model simulations and assessments to establish a validated model for the inflow conditions at the Scaled Wind Farm Technology Facility (SWiFT), located at Texas Tech University in Lubbock, Texas. The collected field data observed at SWiFT display ranges of atmospheric behaviors. The project spans March 2015 through September 2018.

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Offshore Code Comparison Collaboration, Continued, with Correlation

The Offshore Code Comparison, Collaboration, Continued, with Correlation (OC5) is an international research project run under the International Energy Agency (IEA) Wind Task 30. The project is focused on validating the tools used design offshore wind systems. OC5 consists of four phases:

• Phase Ia: analysis of a suspended cylinder in a wave basin

• Phase Ib: analysis of a fixed-bottom cylinder in a wave basin

• Phase II: analysis of a scaled wind turbine on a floating semisubmersible in an ocean basin

• Phase III: analysis of a full-scale wind turbine on a fixed-bottom structure in the open ocean

This website provides the data used for validation in these four phases, as well as simulation results from multiple participants.

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Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty

The Offshore Code Comparison, Collaboration, Continued, with Correlation and unCertainty (OC6) is an international research project run under the International Energy Agency (IEA) Wind Task 30. The project is focused on validating the tools used to design offshore wind systems. OC6 implements a three-way validation process where both the engineering-level modeling tools and higher-fidelity tools are compared to measurement data. The results will be used to help inform the improvement of engineering-level models, and/or guide the development of future test campaigns.

The OC6 project consists of four phases to be performed over four years (2019-2023):

Phase Ia: Validate the nonlinear hydrodynamic loading on floating offshore wind support structures

Phase Ib: Additional data focused on Phase I from a component-level validation campaign geared towards CFD validation

Phase II: Develop and verify an advanced soil/structure interaction model for representing the pile/foundation interaction

Phase III: Validate the aerodynamic loading on a wind turbine rotor undergoing large motion caused by a floating support structure

Phase IV: Benchmark and validate methods for combining potential flow and viscous hydrodynamic load models for novel floating offshore wind support structures.

This website provides the data used for validation in these four phases, as well as simulation results from multiple participants.

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Position of Offshore Wind Energy Resources

In response to a request from U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy, NOAA's Office of Oceanic and Atmospheric Research (OAR) and the National Weather Service (NWS) have performed research to provide information about the type and spatial density of observations needed to characterize the wind resource in the offshore environment.

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Rotor Aerodynamics, Aeroelastics and Wake (RAAW) Experiment

High-fidelity wind turbine modeling advances necessitate model validation datasets of equally high fidelity. The Rotor Aerodynamics, Aeroelastics & Wake (RAAW) project was designed to address this need. This three-year project runs from October 2021 through October 2024 starting with a field data collection period followed by model validation activities. RAAW is a partnership between GE Renewable Energy and the United States Department of Energy (DOE) through the National Renewable Energy Laboratory (NREL) and Sandia National Laboratories.

The field experiment is designed to make detailed measurements of the inflow, wind turbine response, and the resulting wake. The experimental dataset will be suitable for validating wind turbine models across the fidelity spectrum from actuator disk methods to blade-resolved codes.

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Segmented Ultralight Morphing Rotor, Demonstrator (SUMR-D)

The Segmented Ultralight Morphing Rotor (SUMR) Project ( is a revolutionary horizontal axis wind turbine design concept under Advanced Research Projects Agency–Energy (ARPA-E) sponsorship. The SUMR concept takes advantage of highly flexible blades that deflect significantly under load, and thus enable shedding of destructive forces to survive extreme wind events. While the overall SUMR project includes design and analysis of very large rotor blades, this dataset covers field experiments of the SUMR-D rotor, where ‘D’ stands for demonstrator. The SUMR-D rotor blades are gravo-aero-elastically scaled-down versions (approximately 21 m in length) of much larger blades (106.8 m long), which were designed for a 13 MW, IEC Class IIA (IEC 61400-1) wind turbine (SUMR-13). The SUMR-D blade design was created to validate, through a field measurement campaign, the predicted load response, control system characteristics, and performance of the larger SUMR-13. At about 1:4 scale of the SUMR-13, the SUMR-D rotor blade is ~21 m long and weighs ~575 kg, and was designed to mount on the CART2, a 600 kW research wind turbine installed at the NREL Flatirons Campus near Boulder, CO. The SUMR-D rotor operated only in very light winds (5-12 m/s) with a rated rotor power of about 50 kW.

The DAP ARPA-E SUMR database contains the measurements acquired via field experiments using the SUMR-D rotor installed on the CART2 wind turbine at the NREL Flatirons Campus. The goals of the field experiments were to: 1 collect blade loading response from operational and parked conditions and 2) gather blade deflection data via video camera footage.

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Tools Assessing Performance (TAP)

Accurately predicting performance of distributed wind systems, particularly those with hub heights of 40 m or less, is challenging. Key gaps exist in areas of uncertainty quantification for component reliability, turbine availability, and wind resource, particularly in the area of turbulence and its impact on turbine performance. There is no industry-standardized methodology to document procedures, assumptions, or validation efforts for smaller systems.

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Unsteady Aerodynamics Experiment Phase VI NREL-NASA Ames Wind Tunnel Experiment

The Unsteady Aerodynamics Experiment (UAE) was undertaken to acquire research-grade wind turbine aerodynamics measurements capable of expanding physical comprehension, improving predictive models, and advancing turbine technology and performance. Initiated in 1987 at the National Renewable Energy Laboratory’s (NREL) National Wind Technology Center (NWTC), the UAE evolved through five phases of field experiments during 1987 through 1998.[1-3] Concurrently, analogous wind turbine field experiments aimed at the same objectives were pursued at several laboratories in Europe and Asia.[4,5] Together, these field experiments succeeded in developing procedures and instrumentation for acquiring research-grade measurements in the field test environment and generally confirmed that turbine blades produced flow fields that were highly three-dimensional, strongly separated, and predominantly unsteady.

However, crucial details of the turbine blade flow fields remained obscure, overwhelmed by the temporal and spatial disparities imposed by turbulent atmospheric inflows. To isolate the key blade and rotor fluid mechanics details, atmospheric inflow anomalies needed to be eliminated. Thus, plans were launched simultaneously in the United States and Europe to carry out research-grade turbine aerodynamics experiments in the largest U.S. and EU wind tunnels. Documented and archived in the DAP UAE6 database, the NREL UAE Phase VI experiment was conducted in the National Aeronautics and Space Administration (NASA) 80 ft x 120 ft wind tunnel at the NASA Ames Research Center at Moffett Field, California.[6] The EU Model rotor EXperiment In COntrolled conditions (MEXICO) experiment was carried out in the German-Dutch Wind Tunnels (DNW) Large Low Speed Facility (LLF), located near Marknesse in The Netherlands.[7]

The DAP UAE6 database contains the measurements acquired via experiments in the NASA Ames 80 ft x 120 ft during 2000. The UAE Phase VI wind tunnel experiment objectives were to: 1) acquire research grade aerodynamic and structural measurements 2) on a subscale wind turbine geometrically/dynamically similar to full scale, 3) operating in a controlled, low-turbulence inflow environment.

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Wake Steering Experiment

Planned for May through September 2016, Sandia National Laboratories and the National Renewable Energy Laboratory will execute a joint experimental campaign examining wind farm control at the Scaled Wind Farm Technology (SWiFT) facility, hosted at Texas Tech University in Lubbock, Texas. The experimental campaign will be conducted in two phases. In Phase I, an Offset Controller (OC) will be applied to the upwind wind turbine. This controller applies an offset to a nacelle-based wind direction sensor used for aligning the turbine to the wind direction to achieve a prescribed misalignment to the wind. In Phase II, the controller will be replaced by a Wake Steering Controller (WSC) that uses a look-up table based on the FLOw Redirection and Induction in Steady-state (FLORIS) model to find offsets that produce a desired amount of wake steering. The data collected during both phases will be used to perform initial verification and validation studies on controls-oriented models, such as FLORIS, as well as higher-fidelity wind plant analysis models, such as Simulator fOr Wind Farm Applications (SOWFA). Target data include inflow, wake, and turbine performance and loads.

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Wind Forecast Improvement Project 1

The National Oceanic and Atmospheric Administration (NOAA) led this observational, data assimilation, and modeling study that was designed to demonstrate improvements in the accuracy of short-term (0-6 hour) wind forecasts for wind energy.

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Wind Forecast Improvement Project 2

The Wind Forecast Improvement Project in Complex Terrain, or WFIP2, field campaign officially ended on March 31, 2017. However, some instruments continued to collect data in the field after this date. In general, the data collected after March 31, 2017 have not been quality controlled (QC) and exist on the DAP as z0 data. These data should only be used after discussions with the instrument owners. If any of these data have undergone QC, they will exist as b0 data.

The WFIP2 has maintained two overarching scientific goals:

  1. To improve the physical understanding of atmospheric processes that directly affect wind energy forecasts in areas of complex terrain.
  2. To incorporate the new understanding into a foundational weather forecasting model that improves wind energy forecasts.
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The Wind Integration National Dataset (WIND) Toolkit

The WIND Toolkit is a combination of three datasets that reflect the ramping characteristics, spatial and temporal correlations, capacity factors of simulated wind plants, and are time synchronized with available load profiles. The datasets include a meteorological dataset based on mesoscale Numerical Weather Prediction model data, a wind power production dataset based on mesoscale model data, and a power and wind speed forecast dataset. These sets were created using the Weather Research and Forecasting Model run on a 2-km grid over 126,000 locations across the contiguous United States at a five-minute resolution with accuracy mimicked by reforecasting the years 2007–2013 using industry-standard techniques. This combination offers vast improvement to existing wind datasets by significantly expanding the geographic area considered, the number of sites included, and the temporal resolution of the data to aid in subhourly modeling of power system operations. Moreover, the WIND Toolkit's creation also followed the latest wind energy research science and current industry standards.

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Wind Plant Performance Prediction (WP3) Benchmarking Project

The Wind Plant Performance Prediction (WP3) Benchmark project is a validation exercise for wind farm pre-construction energy assessments. This project represents an unprecedented platform for data sharing and wind industry advancement including preconstruction and operational data from up to 100 or more modern operational wind projects.

Project Goals
  1. Generate accurate, independent benchmarks of pre-construction energy assessments
  2. Improve accuracy and reduce uncertainty in pre-construction energy estimates
  3. Create a platform for sharing data to advance the state-of-the-science for pre-construction energy assessments
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Experimental Planetary Boundary Layer Instrumentation Assessment

Multiple remote sensing devices were compared to reference measurements mounted on the National Oceanic and Atmospheric Administration (NOAA) Boulder Atmospheric Observatory (BAO) near Erie, Colorado. XPIA’s primary data collection period was run for a five-week period from March 2–April 6, 2015.

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