Document Type


Date of Degree

Fall 2009

Degree Name

MS (Master of Science)

Degree In

Industrial Engineering

First Advisor

Andrew Kusiak

First Committee Member

Yong Chen

Second Committee Member

Kate Cowles


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.


Data Mining, Evolutionary Strategy Algorithm, Multi-objective Optimization, Wind Energy, Wind Turbine Vibrations


xv, 114 pages


Includes bibliographical references (pages 110-114).


Copyright 2009 Zijun Zhang