Experimental Modelling of a Floating Offshore Wind Turbine

Christian Lindquist, P. Nielsen, Rikke Pedersen, M. Soltani
{"title":"Experimental Modelling of a Floating Offshore Wind Turbine","authors":"Christian Lindquist, P. Nielsen, Rikke Pedersen, M. Soltani","doi":"10.1109/MMAR.2018.8485979","DOIUrl":null,"url":null,"abstract":"Leading wind turbine manufacturers are increasingly looking at the possibilities of sending offshore wind turbines to deep seas. This can be done using a Floating Offshore Wind Turbine (FOWT). Therefore FOWT is an interesting and timely field of study. The aim of the paper is to use System Identification (SI) to make a data-driven-based model for the FOWT system, located in Offshore Wind & Wave Laboratory at Aalborg University. This is achieved by conducting experiments and analyzing the data. SI is used to analyze data from the experiments and obtain different models. These models are then evaluated based on the fit, the frequency response, autocorrelation and crosscorrelation. Eventually, an AutoRegressive Moving Average and Extra input (ARMAX) model is shown to be the most accurate amongst the analyzed models.","PeriodicalId":201658,"journal":{"name":"2018 23rd International Conference on Methods & Models in Automation & Robotics (MMAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 23rd International Conference on Methods & Models in Automation & Robotics (MMAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMAR.2018.8485979","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Leading wind turbine manufacturers are increasingly looking at the possibilities of sending offshore wind turbines to deep seas. This can be done using a Floating Offshore Wind Turbine (FOWT). Therefore FOWT is an interesting and timely field of study. The aim of the paper is to use System Identification (SI) to make a data-driven-based model for the FOWT system, located in Offshore Wind & Wave Laboratory at Aalborg University. This is achieved by conducting experiments and analyzing the data. SI is used to analyze data from the experiments and obtain different models. These models are then evaluated based on the fit, the frequency response, autocorrelation and crosscorrelation. Eventually, an AutoRegressive Moving Average and Extra input (ARMAX) model is shown to be the most accurate amongst the analyzed models.
浮式海上风力发电机的实验建模
领先的风力涡轮机制造商正越来越多地考虑将海上风力涡轮机送到深海的可能性。这可以使用浮动海上风力涡轮机(FOWT)来完成。因此,FOWT是一个有趣而及时的研究领域。本文的目的是利用系统识别(SI)为位于奥尔堡大学海上风浪实验室的FOWT系统建立一个基于数据驱动的模型。这是通过进行实验和分析数据来实现的。SI用于分析实验数据,得到不同的模型。然后根据拟合、频率响应、自相关和互相关对这些模型进行评估。最后,自回归移动平均和额外输入(ARMAX)模型被证明是最准确的分析模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信