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Results for “WIND PLANT CONTROL”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Analysis of axial‐induction‐based wind plant control using an engineering and a high‐order wind plant model

Abstract Wind turbines are typically operated to maximize their performance without considering the impact of wake effects on nearby turbines. Wind plant control concepts aim to increase overall wind plant performance by coordinating the operation of the turbines. This paper focuses on axial‐induction‐based wind plant control techniques, in which the generator torque or blade pitch degrees of freedom of the wind turbines are adjusted. The paper addresses discrepancies between a high‐order wind plant model and an engineering wind plant model. Changes in the engineering model are proposed to better capture the effects of axial‐induction‐based control shown in the high‐order model. Copyright © 2015 John Wiley & Sons, Ltd.

Annoni, Jennifer↗
DOE OSTI2021

Aeroacoustic Assessment of Wind Plant Controls

The Aeroacoustic Assessment project aims to quantify changes in aeroacoustic noise generation by a utility scale wind turbine operating under imposed yaw offsets common for wake steering and wind plant control strategies. Active plant control utilizing wake deflection control strategies have been shown experimentally and computationally to reliably produce 1-2% of additional annual energy production (AEP) without significant changes in turbine structural loads, the potential impact on aeroacoustic emissions has yet to be quantified or completely understood. Yawed operation of a wind turbine changes the three-dimensional aerodynamic interaction between the rotor blades and the incoming atmospheric flow, leading to changes in noise generation. This work quantifies the extent to which active control induces additional aeroacoustic emissions from additional separation and other flow interaction dynamic effects. Given public concerns about wind turbine noise and the need for observational data required for regulators to establish noise restrictions, we must understand potential acoustic emissions resulting from active control prior to commercial deployment and the development of practical noise reduction methods and technologies. The work outlined in this report details the new aeroacoustic measurement capability developed at NREL and its application to making full-field observations of noise from a utility-scale wind turbine. Preliminary noise modeling with the aeroacoustics module in OpenFAST helped to establish the design of the experiment, specifically targeting the noise observed at observers distributed around the DOE-owned GE 1.5 MW wind turbine. Low-frequency and infrasound measurement capability implemented in this project establish an operational baseline for the wind turbine, setting the stage for future control, rotor blade, and turbine design research.

17 WIND ENERGY↗
DOE OSTI2015

Combining Wind Plant Control With Systems Engineering (Presentation)

This presentation was given at the third Wind Energy Systems Engineering Workshop in Boulder, Colorado, and focused on wind plant controls research, combined optimization, a case study on the Princess Amalia Wind Park, results from the case study, and future work.

17 WIND ENERGY↗
DOE OSTI2019

Chapter 7: Wind-Plant-Controller Design

The main purpose of this chapter is the introduction, explanation, and categorization of wind-farm-control algorithms and present their place in a detailed wind farm simulation framework. Specifically, this section will continue to give an introduction to the current practice in wind-farm operation, and the objectives and turbine control methods presented in the literature for coordinated wind farm control.

17 WIND ENERGY↗
DOE OSTI2021

Machine learning enables national assessment of wind plant controls with implications for land use

Summary As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.

17 WIND ENERGY↗
DOE OSTI2016

Computational Fluid Dynamics Simulation Study of Active Power Control in Wind Plants

This paper presents an analysis performed on a wind plant's ability to provide active power control services using a high-fidelity computational fluid dynamics-based wind plant simulator. This approach allows examination of the impact on wind turbine wake interactions within a wind plant on performance of the wind plant controller. The paper investigates several control methods for improving performance in waked conditions. One method uses wind plant wake controls, an active field of research in which wind turbine control systems are coordinated to account for their wakes, to improve the overall performance. Results demonstrate the challenge of providing active power control in waked conditions but also the potential methods for improving this performance.

17 WIND ENERGY↗
DOE OSTI2021

Demonstration of Wake Steering Through Yaw Control in a Wind Plant Field Experiment: Cooperative Research and Development Final Report, CRADA Number CRD-16-00629

Over the last few decades, wind energy has evolved into a large international industry involving major players in the manufacturing, construction, and utility sectors. Coinciding with the industry’s growth, significant innovation in the technology has resulted in larger turbines with lower associated costs of energy and more complex designs in all subsystems. However, as the deployment of the technology has grown and its role within the electricity sector become more prominent, so have the expectations of the technology in terms of performance, reliability, and cost. The industry currently partitions its efforts into separate paths for turbine design, plant design and development, finance, grid interaction and operation, mitigation of adverse community and environmental impacts, and other areas. One prominent area where this partition is evident is in wind turbine control. Traditionally, each wind turbine in a wind plant has been controlled separately – via its own internal controller using only its own sensors. However, wind turbines in a plant interact with each other through the plant-level fluid dynamics. Wake losses (due to upstream turbines extracting energy from the winds and “waking” downstream turbines) can be up to 10% or even 20% of the gross energy production (if each turbine experienced the free stream wind inflow to the plant). A series of studies and experiments have demonstrated that there is potential for improving energy output at existing plants through plant control methods which seek to optimize total wind plant energy production over the current “greedy” approach where each turbine maximizes its own production. Wake steering induced by yaw offsets (turning the turbine to be out of the plane perpendicular to wind inflow) for upstream turbines has shown significant promise in simulations and wind tunnel experiments. In simulation studies, annual energy production has been shown to increase by 2% or more depending on the particular aspects of the wind plant (turbine spacing, meteorological conditions, etc). This project seeks to demonstrate the potential of plant-level controls via wake steering at a commercial wind plant. This is an important step towards commercialization and industry adoption of this plant-level modeling and analysis capability.

17 WIND ENERGY↗
DOE OSTI2016

Maximization of the annual energy production of wind power plants by optimization of layout and yaw-based wake control: Maximization of wind plant AEP by optimization of layout and wake control

This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant annual energy production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses.

17 WIND ENERGY↗
DOE OSTI2023

Enabling control co-design of the next generation of wind power plants

Abstract. Layout design and wake steering through wind plant control are important and complex components in the design and operation of modern wind power plants. They are currently optimized separately, but with more and more computational and experimental studies demonstrating the gains possible through wake steering, there is a growing need from industry and regulating bodies to combine the layout and control optimization in a co-design process. However, combining these two optimization problems is currently infeasible due to the excessive number of design variables and large solution space. In this article, we present a method that enables the coupled optimization of wind power plant layout and wake steering with no additional computational expense than a traditional layout optimization. We developed a geometric relationship between wind turbines to find an approximate optimal yaw angle, bypassing the need for either a nested or coupled wind plant control optimization. It also provides a significant and immediate improvement to wind power plant design by enabling the co-design of turbine layout and yaw control for wake steering. A small co-designed plant shown in this article produces 0.8 % more energy than its sequentially designed counterpart. This additional energy production comes with no additional infrastructure, turbine hardware, or control software; it is simply the outcome of optimizing the turbine layout and yaw control together, resulting in millions of dollars of additional revenue for the wind power plants of the future.

17 WIND ENERGY↗
DOE OSTI2017

Evaluating Tilt for Wind Farms: Preprint

The objective of this work is to demonstrate the feasibility of tilt in a wind plant. Tilt control, much like other wind plant control strategies, has the potential to improve the performance of a wind plant. Tilt control uses the tilt angle of the turbine to direct the wake above or below the downstream turbines. This paper presents a study of tilt in two- and threeturbine arrays. Specifically, the authors show that the power production of a two-turbine array can be increased by tilting turbines in a specific orientation. When adding more turbines, as is shown with the three-turbine array, the overall percentage of power gain increases. This outcome deviates from some of the results seen in typical wind plant control strategies. Finally, we discuss the impact this type of control strategy has on the aerodynamics in a wind plant. This analysis demonstrates that a good understanding of wake characteristics is necessary to improve the plant's performance. A tilt strategy such as the one presented in this paper may have implications for future control/optimization studies including optimization of hub heights in a wind plant and analysis of deep array effects.

17 WIND ENERGY↗
DOE OSTI2017

Evaluating Tilt for Wind Plants

The objective of this work is to demonstrate the feasibility of tilt in a wind plant. Tilt control, much like other wind plant control strategies, has the potential to improve the performance of a wind plant. Tilt control uses the tilt angle of the turbine to direct the wake above or below the downstream turbines. This paper presents a study of tilt in two- and three-turbine arrays. Specifically, the authors show that the power production of a two-turbine array can be increased by tilting turbines in a specific orientation. When adding more turbines, as is shown with the three-turbine array, the overall percentage of power gain increases. This outcome deviates from some of the results seen in typical wind plant control strategies. Finally, we discuss the impact this type of control strategy has on the aerodynamics in a wind plant. This analysis demonstrates that a good understanding of wake characteristics is necessary to improve the plant's performance. A tilt strategy such as the one presented in this paper may have implications for future control/optimization studies including optimization of hub heights in a wind plant and analysis of deep array effects.

17 WIND ENERGY↗
DOE OSTI2019

Flow Control Leveraging Downwind Rotors for Improved Wind Power Plant Operation

Controlling the air flow within wind power plants has the potential to improve plant performance and is an active area of research in the wind energy control community. In order to develop, test, and tune wind power plant controllers efficiently, an accurate engineering model of the turbine wake dynamics is required. Two elements of flow control are wake steering via yaw and tilt of a turbine. When a turbine is yawed or tilted away from the incoming wind field, the wake shape is changed. This is largely due to shed vortices that produce a curled wake. In this work, the well-known wake engineering model FLOw Redirection and Induction in Steady State (FLORIS) wake engineering model is enhanced to include these curled wake effects due to tilt. Since decay of these vortices has not been previously captured in an engineering model, the authors describe how vortices with decay have been added to FLORIS and how the updated model has been used to study the effects due to tilt in the wake. Results are demonstrated and compared to high-fidelity large-eddy simulations. Potential wind power plant performance gains due to flow control using tilt are investigated across different wind conditions and sites. Preliminary results show power gains by using tilt to implement flow control in a variety of wind distributions and tilt values.

17 WIND ENERGY↗
DOE OSTI2016

Utilisation of real-scale renewable energy test facility for validation of generic wind turbine and wind power plant controller models

This article presents an example of application of a modern test facility conceived for experiments regarding the integration of renewable energy in the power system. The capabilities of the test facility are used to validate dynamic simulation models of wind power plants and their controllers. The models are based on standard and generic blocks. The successful validation of events related to the control of active power (control phenomena in <10 Hz range, including frequency control and power oscillation damping) is described, demonstrating the capabilities of the test facility and drawing the track for future work and improvements.

17 WIND ENERGY↗
DOE OSTI2020

Highlighting the Impact of Yaw Control by Parsing Atmospheric Conditions Based on Total Variation: Preprint

Identification of atmospheric conditions within a multivariate atmospheric dataset is a necessary step in the validation of wind plant control strategies. Most often, operating conditions are characterized in terms of aggregated observations and assume that the atmosphere is 'quasi-steady'. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operation strategies. Controlling observational data for statistical stationarity highlights significant enhancements to the power production of waked turbines under wake steering wind plant control. Considering the combined energy ratio of the turbine to which yaw offsets are prescribed as well as the waked turbine show only moderate improvements when filtering for total validation. The same quality control that highlights wake steering also serves to underpin the off-nominal operation of the controlled turbine.

17 WIND ENERGY↗
DOE OSTI2018

Analysis of control-oriented wake modeling tools using lidar field results

Abstract. The objective of this paper is to compare field data from a scanning lidar mounted on a turbine to control-oriented wind turbine wake models. The measurements were taken from the turbine nacelle looking downstream at the turbine wake. This field campaign was used to validate control-oriented tools used for wind plant control and optimization. The National Wind Technology Center in Golden, CO, conducted a demonstration of wake steering on a utility-scale turbine. In this campaign, the turbine was operated at various yaw misalignment set points, while a lidar mounted on the nacelle scanned five downstream distances. Primarily, this paper examines measurements taken at 2.35 diameters downstream of the turbine. The lidar measurements were combined with turbine data and measurements of the inflow made by a highly instrumented meteorological mast on-site. This paper presents a quantitative analysis of the lidar data compared to the control-oriented wake models used under different atmospheric conditions and turbine operation. Furthermore, these results show that good agreement is obtained between the lidar data and the models under these different conditions.

17 WIND ENERGY↗
DOE OSTI2015

Simulation Comparison of Wake Mitigation Control Strategies for a Two-Turbine Case

Wind turbines arranged in a wind plant impact each other through their wakes. Wind plant control is an active research field that attempts to improve wind plant performance by coordinating control of individual turbines to take into account these turbine–wake interactions. High-fidelity simulations of a two-turbine fully waked scenario are used to investigate several wake mitigation strategies, in this paper, including modification of yaw and tilt angles of an upstream turbine to induce wake skew, as well as repositioning of the downstream turbine. The simulation results are compared through change relative to a baseline operation in terms of overall power capture and loading on the upstream and downstream turbine. Results demonstrated improved power production for all methods. Moreover, analysis of control options, including individual pitch control, shows potential to minimize the increase of, or even reduce, turbine loads.

17 WIND ENERGY↗