基于ARMA模型的海上风机随机风场模拟
Simulation of stochastic wind field of offshore wind turbines based on ARMA model
李鹏 1朱洪泽 2骆光杰 1苏凯3
作者信息
- 1. 浙江华东工程咨询有限公司,浙江杭州,310014
- 2. 武汉大学水资源工程与调度全国重点实验室,湖北武汉,430072
- 3. 武汉大学水资源工程与调度全国重点实验室,湖北武汉,430072;武汉大学水工岩石力学教育部重点实验室,湖北武汉,430072;武汉大学海绵城市建设水系统湖北省重点实验室,湖北武汉,430072
- 折叠
摘要
为分析风荷载作用下的海上风机结构动力响应特性,提出采用数值仿真的方法模拟其空间域内的随机风场.利用MATLAB软件编制基于ARMA(autoregressive moving average)模型的脉动风速时程模拟程序,并嵌入AIC(Akaike's information theoretic)准则以判定ARMA模型的最优阶数,最终,针对江苏黄海某海上风机项目进行数值仿真分析,将模拟脉动风速功率谱与目标Davenport谱进行对比.计算结果表明:根据AIC准则确定的ARMA模型最优阶数可兼顾模型模拟精度和计算成本;模拟脉动风速时程功率谱函数在0.3 Hz以下的低频区间波动较大,与目标谱函数存在一定差距,在中高频段两者规律差距较小,整体模拟精度较高.
Abstract
A numerical simulation method for the stochastic wind field in a certain spatial domain was proposed to analyze the dynamic response characteristics of offshore wind turbines under wind loads.The simulation program for the time-history series of fluctuating wind speed was compiled by using the MATLAB software based on ARMA(autoregressive moving average)model.The optimal order of the ARMA model was determined through embedding the AIC(Akaike's information criterion).Finally,a numerical simulation analysis was carried out for an actual offshore wind turbine project in the Yellow Sea of Jiangsu Province,and the power spectrum of the simulated fluctuating wind velocity was compared with the target Davenport spectrum.The calculation results show that the optimal rank of the ARMA model determined according to the AIC can take into account the simulation precision and computation cost.The power spectrum function of simulated fluctuating wind speed fluctuates greatly in the low frequency range below 0.3 Hz,and there is a certain gap with the target spectrum function.The two laws are consistent,and the gap is small in the medium and high frequency ranges.The overall simulation accuracy is high.
关键词
海上风机/ARMA模型/AIC/Davenport功率谱/MATLAB/脉动风速时程Key words
offshore wind turbines/ARMA model/Akaike's information criterion/Davenport power spectrum/MATLAB/fluctuating wind speed time series引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据