TY - GEN
T1 - Uncertainty quantification of wind turbine wakes under random wind conditions
AU - Pereira, Tássia Penha
AU - Ekwaro-Osire, Stephen
AU - Dias, João Paulo
AU - Ward, Nicholas J.
AU - Cunha, Americo
N1 - Publisher Copyright:
Copyright © 2019 ASME.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - Understanding and minimizing the uncertainties in the wind energy field is of high importance to reduce the reliability risks and financial risks of wind farm projects. The present work aims to observe the levels of uncertainty in modeling the wake effect by attempting to perform statistical inference of a wake parameter, the wind speed deficit. For this purpose, an uncertainty propagation framework is presented. The framework starts by randomly sampling mean wind speed data from its probability density function (PDF), that is fed an inflow model (TurbSim), resulting in random full-flow fields that are integrated into an aeroelastic model (FAST), which results in the variability of the power and thrust coefficients of a wind turbine. Such coefficients and wind data, finally, fed the wake engineering model (FLORIS). The framework ends with the determination of the 95% coefficient intervals of the time-averaged wind speed deficit. The results obtained for the near and far wake regions introduce fundamentals in estimate the uncertainty in wind speed deficit of a single wind turbine wake and concludes that a systematic uncertainty quantification (UQ) framework for wind turbine wakes may be a useful tool to wind energy projects.
AB - Understanding and minimizing the uncertainties in the wind energy field is of high importance to reduce the reliability risks and financial risks of wind farm projects. The present work aims to observe the levels of uncertainty in modeling the wake effect by attempting to perform statistical inference of a wake parameter, the wind speed deficit. For this purpose, an uncertainty propagation framework is presented. The framework starts by randomly sampling mean wind speed data from its probability density function (PDF), that is fed an inflow model (TurbSim), resulting in random full-flow fields that are integrated into an aeroelastic model (FAST), which results in the variability of the power and thrust coefficients of a wind turbine. Such coefficients and wind data, finally, fed the wake engineering model (FLORIS). The framework ends with the determination of the 95% coefficient intervals of the time-averaged wind speed deficit. The results obtained for the near and far wake regions introduce fundamentals in estimate the uncertainty in wind speed deficit of a single wind turbine wake and concludes that a systematic uncertainty quantification (UQ) framework for wind turbine wakes may be a useful tool to wind energy projects.
KW - Computational model
KW - Parametric statistics
KW - Reliability
KW - Uncertainty quantification
KW - Wind energy efficiency
KW - Wind turbine wakes
UR - http://www.scopus.com/inward/record.url?scp=85078705928&partnerID=8YFLogxK
U2 - 10.1115/IMECE2019-11872
DO - 10.1115/IMECE2019-11872
M3 - Conference contribution
AN - SCOPUS:85078705928
T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
BT - Safety Engineering, Risk, and Reliability Analysis
PB - American Society of Mechanical Engineers (ASME)
Y2 - 11 November 2019 through 14 November 2019
ER -