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Title: Wind direction estimation using SCADA data with consensus-based optimization

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. To enable 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 a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm 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 article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rathermore » than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.« less

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [3];  [4]; ORCiD logo [1];  [1]; ORCiD logo [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States); Colorado School of Mines, Golden, CO (United States); Univ. of Colorado, Boulder, CO (United States)
  3. National Renewable Energy Lab. (NREL), Golden, CO (United States); Colorado School of Mines, Golden, CO (United States)
  4. Univ. of Colorado, Boulder, CO (United States)
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:
1544528
Report Number(s):
NREL/JA-5000-74366
Journal ID: ISSN 2366-7451
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Wind Energy Science (Online)
Additional Journal Information:
Journal Name: Wind Energy Science (Online); Journal Volume: 4; Journal Issue: 2; Journal ID: ISSN 2366-7451
Publisher:
European Wind Energy Association - Copernicus
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind farm control; distributed optimization

Citation Formats

Annoni, Jennifer R., Bay, Christopher, Johnson, Kathryn E., Dall'Anese, Emiliano, Quon, Eliot W., Kemper, Travis W., and Fleming, Paul A. Wind direction estimation using SCADA data with consensus-based optimization. United States: N. p., 2019. Web. doi:10.5194/wes-4-355-2019.
Annoni, Jennifer R., Bay, Christopher, Johnson, Kathryn E., Dall'Anese, Emiliano, Quon, Eliot W., Kemper, Travis W., & Fleming, Paul A. Wind direction estimation using SCADA data with consensus-based optimization. United States. doi:10.5194/wes-4-355-2019.
Annoni, Jennifer R., Bay, Christopher, Johnson, Kathryn E., Dall'Anese, Emiliano, Quon, Eliot W., Kemper, Travis W., and Fleming, Paul A. Thu . "Wind direction estimation using SCADA data with consensus-based optimization". United States. doi:10.5194/wes-4-355-2019. https://www.osti.gov/servlets/purl/1544528.
@article{osti_1544528,
title = {Wind direction estimation using SCADA data with consensus-based optimization},
author = {Annoni, Jennifer R. and Bay, Christopher and Johnson, Kathryn E. and Dall'Anese, Emiliano and Quon, Eliot W. and Kemper, Travis W. and Fleming, Paul A.},
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. To enable 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 a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm 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 article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.},
doi = {10.5194/wes-4-355-2019},
journal = {Wind Energy Science (Online)},
number = 2,
volume = 4,
place = {United States},
year = {2019},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Figure 1 Figure 1: (a) Example six-turbine wind farm. (b) Example clustering based on the nearest three neighbors. The different colors represent the different clusters. For example, the darker blue lines indicate that Turbines 2, 3, and 4 are communicating with Turbine 5.

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Works referenced in this record:

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