Date of Degree
2009
Document Type
thesis
Degree Name
MS (Master of Science)
Department
Industrial Engineering
First Advisor
Andrew Kusiak
Abstract
Vibrations of a wind turbine have a negative impact on its performance and therefore approaches to effectively control turbine vibrations are sought by wind industry. The body of previous research on wind turbine vibrations has focused on physics-based models. Such models come with limitations as some ideal assumptions do not reflect reality. In this Thesis a data-driven approach to analyze the wind turbine vibrations is introduced.
Improvements in the data collection of information system allow collection of large volumes of industrial process data. Although the sufficient information is contained in collected data, they cannot be fully utilized to solve the challenging industrial modeling issues. Data-mining is a novel science offers platform to identify models or recognize patterns from large data set. Various successful applications of data mining proved its capability in extracting models accurately describing the processes of interest.
The vibrations of a wind turbine originate at various sources. This Thesis focuses on mitigating vibrations with wind turbine control. Data mining algorithms are utilized to construct vibration models of a wind turbine that are represented by two parameters, drive train acceleration and tower acceleration. An evolutionary strategy algorithm is employed to optimize the wind turbine performance expressed with three objectives, power generation, vibration of wind turbine drive train, and vibration of wind turbine tower.
The methodology presented in this Thesis is applicable to industrial processes other than wind industry.
Pages
xv, 114
Bibliography
110-114
Copyright
Copyright 2009 Zijun Zhang
Recommended Citation
Zhang, Zijun. "Wind turbine vibration study: a data driven methodology." thesis, University of Iowa, 2009.
http://ir.uiowa.edu/etd/454.