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Title: Lidar-Enhanced Wind Turbine Control: Past, Present, and Future: Preprint

Abstract

This paper will look at the development of lidar-enhanced controls and how they have been used for turbine load reduction with pitch actuation, as well as increased energy production with improved yaw control. Ongoing work will also be discussed to show that combining pitch and torque control using feedforward nonlinear model predictive control can lead to both reduced loads and increased energy production. Future work is also proposed on extending individual wind turbine controls to the wind plant level and determining how lidars can be used for control methods to further lower the cost of wind energy by minimizing wake impacts in a wind farm.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1266694
Report Number(s):
NREL/CP-5000-65879
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2016 American Control Conference, 6-8 July 2016, Boston, Massachusetts
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; lidar control; wind turbine; feedforward; wind energy; wind farm; wind plant; levelized cost of energy; NREL

Citation Formats

Scholbrock, Andrew, Fleming, Paul, Wright, Alan, Wang, Na, Schlipf, David, and Johnson, Kathryn. Lidar-Enhanced Wind Turbine Control: Past, Present, and Future: Preprint. United States: N. p., 2016. Web. doi:10.1109/ACC.2016.7525113.
Scholbrock, Andrew, Fleming, Paul, Wright, Alan, Wang, Na, Schlipf, David, & Johnson, Kathryn. Lidar-Enhanced Wind Turbine Control: Past, Present, and Future: Preprint. United States. doi:10.1109/ACC.2016.7525113.
Scholbrock, Andrew, Fleming, Paul, Wright, Alan, Wang, Na, Schlipf, David, and Johnson, Kathryn. 2016. "Lidar-Enhanced Wind Turbine Control: Past, Present, and Future: Preprint". United States. doi:10.1109/ACC.2016.7525113. https://www.osti.gov/servlets/purl/1266694.
@article{osti_1266694,
title = {Lidar-Enhanced Wind Turbine Control: Past, Present, and Future: Preprint},
author = {Scholbrock, Andrew and Fleming, Paul and Wright, Alan and Wang, Na and Schlipf, David and Johnson, Kathryn},
abstractNote = {This paper will look at the development of lidar-enhanced controls and how they have been used for turbine load reduction with pitch actuation, as well as increased energy production with improved yaw control. Ongoing work will also be discussed to show that combining pitch and torque control using feedforward nonlinear model predictive control can lead to both reduced loads and increased energy production. Future work is also proposed on extending individual wind turbine controls to the wind plant level and determining how lidars can be used for control methods to further lower the cost of wind energy by minimizing wake impacts in a wind farm.},
doi = {10.1109/ACC.2016.7525113},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 7
}

Conference:
Other availability
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  • The main challenges in harvesting energy from the wind arise from the unknown incoming turbulent wind field. Balancing the competing interests of reduction in structural loads and increasing energy production is the goal of a wind turbine controller to reduce the cost of producing wind energy. Conventional wind turbines use feedback methods to optimize these goals, reacting to wind disturbances after they have already impacted the wind turbine. Lidar sensors offer a means to provide additional inputs to a wind turbine controller, enabling new techniques to improve control methods, allowing a controller to actuate a wind turbine in anticipation ofmore » an incoming wind disturbance. This paper will look at the development of lidar-enhanced controls and how they have been used for various turbine load reductions with pitch actuation, as well as increased energy production with improved yaw control. Ongoing work will also be discussed to show that combining pitch and torque control using feedforward nonlinear model predictive control can lead to both reduced loads and increased energy production. Future work is also proposed on extending individual wind turbine controls to the wind plant level and determining how lidars can be used for control methods to further lower the cost of wind energy by minimizing wake impacts in a wind farm.« less
  • A severe challenge in controlling wind turbines is ensuring controller performance in the presence of a stochastic and unknown wind field, relying on the response of the turbine to generate control actions. Recent technologies such as LIDAR, allow sensing of the wind field before it reaches the rotor. In this work a field-testing campaign to test LIDAR Assisted Control (LAC) has been undertaken on a 600-kW turbine using a fixed, five-beam LIDAR system. The campaign compared the performance of a baseline controller to four LACs with progressively lower levels of feedback using 35 hours of collected data.
  • Turbine-mounted lidars provide preview measurements of the incoming wind field. By reducing loads on critical components and increasing the potential power extracted from the wind, the performance of wind turbine controllers can be improved [2]. As a result, integrating a light detection and ranging (lidar) system has the potential to lower the cost of wind energy. This paper presents an evaluation of turbine-mounted lidar availability. Availability is a metric which measures the proportion of time the lidar is producing controller-usable data, and is essential when a wind turbine controller relies on a lidar. To accomplish this, researchers from Avent Lidarmore » Technology and the National Renewable Energy Laboratory first assessed and modeled the effect of extreme atmospheric events. This shows how a multirange lidar delivers measurements for a wide variety of conditions. Second, by using a theoretical approach and conducting an analysis of field feedback, we investigated the effects of the lidar setup on the wind turbine. This helps determine the optimal lidar mounting position at the back of the nacelle, and establishes a relationship between availability, turbine rpm, and lidar sampling time. Lastly, we considered the role of the wind field reconstruction strategies and the turbine controller on the definition and performance of a lidar's measurement availability.« less
  • We review the objectives and techniques used in the control of horizontal axis wind turbines at the individual turbine level, where controls are applied to the turbine blade pitch and generator. The turbine system is modeled as a flexible structure operating in the presence of turbulent wind disturbances. Some overview of the various stages of turbine operation and control strategies used to maximize energy capture in below rated wind speeds is given, but emphasis is on control to alleviate loads when the turbine is operating at maximum power. After reviewing basic turbine control objectives, we provide an overview of themore » common basic linear control approaches and then describe more advanced control architectures and why they may provide significant advantages.« less
  • This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited, or can even result in harmful control action. An online analysis of the lidar and turbine data are necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the predictionmore » time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross correlation to determine the time shift between both signals. Further, initial results from an ongoing campaign in which this system was employed for providing lidar preview for feed-forward pitch control are presented.« less