Abstract
Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This code implements a framework for implementing an autonomous wind farm that incorporates information from local sensors in real time to better align turbines in a wind farm. Oftentimes, measurements made at an individual turbine are noisy and unreliable. By incorporating measurements from multiple nearby turbines, a more robust estimate of the wind direction can be obtained at an individual turbine. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction and could decrease dynamic
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- Developers:
-
King, Jennifer [1] ; Bay, Christopher [1] ; Johnson, Kathryn [1]
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Release Date:
- 2019-02-14
- Project Type:
- Closed Source
- Software Type:
- Scientific
- Programming Languages:
-
Python
- Sponsoring Org.:
-
USDOE Laboratory Directed Research and Development (LDRD) ProgramPrimary Award/Contract Number:AC36-08GO28308
- Code ID:
- 78027
- Site Accession Number:
- SWR-19-11
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
King, Jennifer, Bay, Christopher, and Johnson, Kathryn.
Consensus Control (Wind Farm Collective Yaw Control) [SWR-19-11].
Computer Software.
USDOE Laboratory Directed Research and Development (LDRD) Program.
14 Feb. 2019.
Web.
doi:10.11578/dc.20230307.4.
King, Jennifer, Bay, Christopher, & Johnson, Kathryn.
(2019, February 14).
Consensus Control (Wind Farm Collective Yaw Control) [SWR-19-11].
[Computer software].
https://doi.org/10.11578/dc.20230307.4.
King, Jennifer, Bay, Christopher, and Johnson, Kathryn.
"Consensus Control (Wind Farm Collective Yaw Control) [SWR-19-11]." Computer software.
February 14, 2019.
https://doi.org/10.11578/dc.20230307.4.
@misc{
doecode_78027,
title = {Consensus Control (Wind Farm Collective Yaw Control) [SWR-19-11]},
author = {King, Jennifer and Bay, Christopher and Johnson, Kathryn},
abstractNote = {Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This code implements a framework for implementing an autonomous wind farm that incorporates information from local sensors in real time to better align turbines in a wind farm. Oftentimes, measurements made at an individual turbine are noisy and unreliable. By incorporating measurements from multiple nearby turbines, a more robust estimate of the wind direction can be obtained at an individual turbine. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction and could decrease dynamic yaw misalignment, decrease the amount of time a turbine spends yawing due to a more robust input to the yaw controller, and increase resiliency to faulty wind-vane measurements.},
doi = {10.11578/dc.20230307.4},
url = {https://doi.org/10.11578/dc.20230307.4},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20230307.4}},
year = {2019},
month = {feb}
}