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.