Open AccessTowards an improved understanding of statistical extrapolation for wind turbine extreme loads
Author(s) -
Fogle Jeffrey,
Agarwal Puneet,
Manuel Lance
Publication year - 2008
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.303
Subject(s) - extrapolation , maxima , turbine , block (permutation group theory) , extreme value theory , aeroelasticity , wind power , engineering , structural engineering , computer science , control theory (sociology) , mathematics , statistics , aerodynamics , mechanical engineering , aerospace engineering , electrical engineering , control (management) , art history , art , geometry , artificial intelligence , performance art
One of the load cases that must be evaluated per the International Electrotechnical Commission standard for wind turbine design requires that characteristic loads associated with a 50‐year return period be established. This is usually done by carrying out aeroelastic response simulations of the turbine. In order to estimate such rare loads, extreme loads data of adequate quantity and quality are required to facilitate robust predictions. Practitioners have expressed concerns about aspects of the load extrapolation—for instance, questions have arisen related to the minimum number of required ten‐minute turbine response simulations, about whether only a single (global) maximum load from each simulation should be saved or whether, alternatively, several time‐separated (block) maxima are preferred. Also, though turbine load types are not influenced by each wind speed between cut‐in and cut‐out to the same degree, focused simulation effort on winds that control the largest loads for each load type is not addressed. Using global and block maxima for four load measures from aeroelastic simulations on a 5 MW turbine model, we study short‐term load distributions as a function of wind speed. Block maxima for different block sizes (time separations) are tested for independence and empirical load distributions for global and block maxima are compared. We present a proposal for addressing load extrapolation that focuses on efficiency, that spells out how to employ either global or block load maxima, and that provides convergence criteria for deciding on an adequate number of simulations that must be performed before attempting long‐term load prediction using extrapolation. Copyright © 2008 John Wiley & Sons, Ltd.