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Title: Short-Term Forecasting Across a Network for the Autonomous Wind Farm: Preprint

Abstract

In an autonomous wind farm, turbines will use information from nearby turbines to achieve wind farm-level objectives such as optimizing the overall performance of a wind farm, ensuring resiliency when other sensors fail, and adapting to changing local conditions. In this paper, the wind farm can be modeled as a network within which turbines (nodes) share information across designated communication channels, with a focus on turbines at the outside of the wind farm capturing local effects and sharing that information with downstream turbines. Understanding of varied inflow conditions can be especially important in complex terrain. This information can be used to monitor turbines, self-organize turbines into groups, and predict the power performance of a wind farm. In particular, this paper describes an autonomous wind farm that incorporates information from local sensors in real time to predict wind speed and wind direction at each turbine over a short-term horizon. Results indicate that the estimate of wind direction can be used to improve the knowledge of the wind speed and direction over the persistence method on a 10- 15-minute time horizon. These short-term forecasts can also be used to facilitate advanced control methods such as feedforward control within a wind farm.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. National Renewable Energy Laboratory (NREL), Golden, 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:
1547237
Report Number(s):
NREL/CP-5000-73396
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the IEEE American Control Conference, 10-12 July 2019, Philadelphia, Pennsylvania
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind energy; wind farm control; distributed optimization; short-term forecasting; performance

Citation Formats

King, Jennifer R, Bay, Christopher, Johnson, Kathryn E, and Fleming, Paul A. Short-Term Forecasting Across a Network for the Autonomous Wind Farm: Preprint. United States: N. p., 2019. Web.
King, Jennifer R, Bay, Christopher, Johnson, Kathryn E, & Fleming, Paul A. Short-Term Forecasting Across a Network for the Autonomous Wind Farm: Preprint. United States.
King, Jennifer R, Bay, Christopher, Johnson, Kathryn E, and Fleming, Paul A. Wed . "Short-Term Forecasting Across a Network for the Autonomous Wind Farm: Preprint". United States. https://www.osti.gov/servlets/purl/1547237.
@article{osti_1547237,
title = {Short-Term Forecasting Across a Network for the Autonomous Wind Farm: Preprint},
author = {King, Jennifer R and Bay, Christopher and Johnson, Kathryn E and Fleming, Paul A},
abstractNote = {In an autonomous wind farm, turbines will use information from nearby turbines to achieve wind farm-level objectives such as optimizing the overall performance of a wind farm, ensuring resiliency when other sensors fail, and adapting to changing local conditions. In this paper, the wind farm can be modeled as a network within which turbines (nodes) share information across designated communication channels, with a focus on turbines at the outside of the wind farm capturing local effects and sharing that information with downstream turbines. Understanding of varied inflow conditions can be especially important in complex terrain. This information can be used to monitor turbines, self-organize turbines into groups, and predict the power performance of a wind farm. In particular, this paper describes an autonomous wind farm that incorporates information from local sensors in real time to predict wind speed and wind direction at each turbine over a short-term horizon. Results indicate that the estimate of wind direction can be used to improve the knowledge of the wind speed and direction over the persistence method on a 10- 15-minute time horizon. These short-term forecasts can also be used to facilitate advanced control methods such as feedforward control within a wind farm.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {7}
}

Conference:
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