Probabilistic Response and Short-Term Extreme Load Estimation of Offshore Monopile Wind Turbine Towers by Probability Density Evolution Method  

作　　者：ZHANG Hui XU Ya-zhou 

机构地区：[1]School of Building Services Science and Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China [2]School of Civil Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China

出　　处：《China Ocean Engineering》2022年第3期363-371,共9页中国海洋工程（英文版）

基　　金：This research is supported by the National Natural Science Foundation of China(Grant No.51578444);Key Science Research Program of Education Department of Shaanxi Province(Grant No.20JY032).

摘　　要：A new analysis framework based on probability density evolution method(PDEM)and its Chebyshev collocation solution are introduced to predict the dynamic response and short-term extreme load of offshore wind turbine(OWT)towers subjected to random sea state.With regard to the stochastic responses,random function method is employed to generate samples of sea elevation,the probability density evolution equation(PDEE)is solved to calculate time-variant probability density functions of structural responses.For the probabilistic load estimation,a FAST model of NREL 5MW offshore turbine is established to obtain samples of bending moment at the tower base.The equivalent extreme event theory is used to construct a virtual stochastic process(VSP)to assess the short-term extreme load.The results indicate that the proposed approach can predict time-variant probability density functions of the structural responses,and shows good agreement with Monte Carlo simulations.Additionally,the predicted short-term extreme load can capture the fluctuation at the tail of the extreme value distribution,thus is more rational than results from the typical distribution models.Overall,the proposed method shows good adaptation,precision and efficiency for the dynamic response analysis and load estimation of OWT towers.

关 键 词：offshore wind turbine probability density evolution method Chebyshev collocation matrix exponential Monte Carlo 

分 类 号：TM614[电气工程—电力系统及自动化] P75[天文地球—海洋科学]