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Title: A data-driven method to characterize turbulence-caused uncertainty in wind power generation

Abstract

A data-driven methodology is developed to analyze how ambient and wake turbulence affect the power generation of wind turbine(s). Using supervisory control and data acquisition (SCADA) data from a wind plant, we select two sets of wind velocity and power data for turbines on the edge of the plant that resemble (i) an out-of-wake scenario and (ii) an in-wake scenario. For each set of data, two surrogate models are developed to represent the turbine(s) power generation as a function of (i) the wind speed and (ii) the wind speed and turbulence intensity. Three types of uncertainties in turbine(s) power generation are investigated: (i) the uncertainty in power generation with respect to the reported power curve; (ii) the uncertainty in power generation with respect to the estimated power response that accounts for only mean wind speed; and (iii) the uncertainty in power generation with respect to the estimated power response that accounts for both mean wind speed and turbulence intensity. Results show that (i) the turbine(s) generally produce more power under the in-wake scenario than under the out-of-wake scenario with the same wind speed; and (ii) there is relatively more uncertainty in the power generation under the in-wake scenario than undermore » the out-of-wake scenario.« less

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)
OSTI Identifier:
1290784
Report Number(s):
NREL/JA-5D00-66925
Journal ID: ISSN 0360-5442
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Journal Name:
Energy (Oxford)
Additional Journal Information:
Journal Volume: 112; Journal ID: ISSN 0360-5442
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; data-driven; surrogate modeling; uncertainty quantification; turbulence intensity; wind distribution

Citation Formats

Zhang, Jie, Jain, Rishabh, and Hodge, Bri-Mathias. A data-driven method to characterize turbulence-caused uncertainty in wind power generation. United States: N. p., 2016. Web. doi:10.1016/j.energy.2016.06.144.
Zhang, Jie, Jain, Rishabh, & Hodge, Bri-Mathias. A data-driven method to characterize turbulence-caused uncertainty in wind power generation. United States. https://doi.org/10.1016/j.energy.2016.06.144
Zhang, Jie, Jain, Rishabh, and Hodge, Bri-Mathias. 2016. "A data-driven method to characterize turbulence-caused uncertainty in wind power generation". United States. https://doi.org/10.1016/j.energy.2016.06.144.
@article{osti_1290784,
title = {A data-driven method to characterize turbulence-caused uncertainty in wind power generation},
author = {Zhang, Jie and Jain, Rishabh and Hodge, Bri-Mathias},
abstractNote = {A data-driven methodology is developed to analyze how ambient and wake turbulence affect the power generation of wind turbine(s). Using supervisory control and data acquisition (SCADA) data from a wind plant, we select two sets of wind velocity and power data for turbines on the edge of the plant that resemble (i) an out-of-wake scenario and (ii) an in-wake scenario. For each set of data, two surrogate models are developed to represent the turbine(s) power generation as a function of (i) the wind speed and (ii) the wind speed and turbulence intensity. Three types of uncertainties in turbine(s) power generation are investigated: (i) the uncertainty in power generation with respect to the reported power curve; (ii) the uncertainty in power generation with respect to the estimated power response that accounts for only mean wind speed; and (iii) the uncertainty in power generation with respect to the estimated power response that accounts for both mean wind speed and turbulence intensity. Results show that (i) the turbine(s) generally produce more power under the in-wake scenario than under the out-of-wake scenario with the same wind speed; and (ii) there is relatively more uncertainty in the power generation under the in-wake scenario than under the out-of-wake scenario.},
doi = {10.1016/j.energy.2016.06.144},
url = {https://www.osti.gov/biblio/1290784}, journal = {Energy (Oxford)},
issn = {0360-5442},
number = ,
volume = 112,
place = {United States},
year = {Sat Oct 01 00:00:00 EDT 2016},
month = {Sat Oct 01 00:00:00 EDT 2016}
}