Open AccessIncorporating a stochastic data‐driven inflow model for uncertainty quantification of wind turbine performance
Author(s) -
Guo Q.,
Ganapathysubramanian B.
Publication year - 2017
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.2108
Subject(s) - inflow , uncertainty quantification , turbine , computer science , wind power , uncertainty analysis , stochastic simulation , stochastic modelling , reliability (semiconductor) , solver , probability distribution , engineering , simulation , meteorology , mathematics , statistics , power (physics) , aerospace engineering , physics , electrical engineering , machine learning , quantum mechanics , programming language
Incorporating uncertainty in wind turbine analysis and design is very necessary based on the fact that inherent variability exists in wind turbine systems. Examples of these uncertainties include fluctuations in material properties across turbine blades, variable structure parameters and stochasticity in the inflow—which is considered to be a critical factor affecting the reliability of wind turbines. However, it has been difficult to construct a low‐dimensional yet accurate representation of the stochastic inflow, which precludes rigorous uncertainty propagation and quantification. Recently, we have developed a comprehensive data‐driven approach [called temporal–spatial decomposition (TSD)] for constructing a stochastic, low‐dimensional model that accurately represents stochastic inflow data. We leverage this approach to construct distributional forecasts of key wind turbine performance indicators. To this end, we integrated the stochastic wind model created by the TSD framework with the wind turbine solver FAST. Uncertainty propagation is performed using an adaptive sparse grid collocation approach. We investigate how the order of approximation of the stochastic model affects the quality of the predicted distribution. We observe that the probability distributions of key indicators are not necessarily Gaussian, which has implications for reliability analysis and for failure prediction. Furthermore, the distributions are sensitive to only the first few eigenmodes of the inflow wind model, which indicates that comprehensive uncertainty quantification can potentially be accomplished with moderate computational effort. The approach suggested in this paper enables seamless integration of uncertainty quantification into current deterministic codes for wind turbine simulation and has implications for the design of the next generation of wind turbines including offshore turbines. Copyright © 2017 John Wiley & Sons, Ltd.