IEA Wind Task 32 serves as an international platform for the research community and industry to identify and mitigate barriers to the use of lidars in wind energy applications. The workshop "Optimizing Lidar Design for Wind Energy Applications" was held in July 2016 to identify lidar system properties that are desirable for wind turbine control applications and help foster the widespread application of lidar-assisted control (LAC). One of the main barriers this workshop aimed to address is the multidisciplinary nature of LAC. Since lidar suppliers, wind turbine manufacturers, and researchers typically focus on their own areas of expertise, it is possible that current lidar systems are not optimal for control purposes. This paper summarizes the results of the workshop, addressing both practical and theoretical aspects, beginning with a review of the literature on lidar optimization for control applications. Next, barriers to the use of lidar for wind turbine control are identified, such as availability and reliability concerns, followed by practical suggestions for mitigating those barriers. From a theoretical perspective, the optimization of lidar scan patterns by minimizing the error between the measurements and the rotor effective wind speed of interest is discussed. Frequency domain methods for directly calculating measurement error using a stochastic wind field model are reviewed and applied to the optimization of several continuous wave and pulsed Doppler lidar scan patterns based on commercially-available systems. An overview of the design process for a lidar-assisted pitch controller for rotor speed regulation highlights design choices that can impact the usefulness of lidar measurements beyond scan pattern optimization. Finally, using measurements from an optimized scan pattern, it is shown that the rotor speed regulation achieved after optimizing the lidar-assisted control scenario via time domain simulations matches the performance predicted by the theoretical frequency domain model.
Researchers at the National Renewable Energy Laboratory (NREL) and the University ofStuttgart are designing, implementing, and testing advanced feedback and feed-forward controls for multimegawatt wind turbines that will help reduce the cost of wind energy. Past wind turbine controllers have depended on turbine feedback measurements to determine the controller pitch commands. In this setup, wind speed disturbances can only be corrected after their effects have been detected in the turbine's loads and dynamic response, which causes a delayed control response due to turbine and pitch actuator dynamics. LIght Detection And Ranging (LIDAR) systems can provide information regarding the approaching wind field to the controller in advance, thereby increasing the controller's available reaction time and allowing pitch actuation to occur in advance to mitigate wind disturbance effects. Feed-forward control algorithms that use these "look ahead" wind speed measurements can improve load mitigation and controller performance compared to feedback only controllers. This paper describes the development and field testing of a feed-forward collective pitch control algorithm to show its effects on speed regulation in above-rated wind speeds. The controller is implemented and field tested on one of the Controls Advanced Research Turbines (CARTs) at NREL. The wind speed measurements to the feed-forward controller are provided by BlueScout Technologies' Optical Control System (OCS) LIDAR mounted on the nacelle of the CART3. Results show that inclusion of the LIDAR measurement into the control system leads to further rejection of the wind disturbance at low frequencies when compared to feedback alone. This in turn provides confidence that LIDAR technology could be used to obtain load reductions with wind turbine controls.
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. These results show that good agreement is obtained between the lidar data and the models under these different conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.