Wind turbine vibration study: a data driven methodology

Recommended Citation Zhang, Zijun. "Wind turbine vibration study: a data driven methodology. ii To My Parents and Family iii Hope is a waking dream. Aristotle iv ACKNOWLEDGMENTS I would like to express my sincere gratitude to my advisor Professor Andrew Kusiak, for his devotion to this research. He has been the most instrumental person for my academic and research achievements. He provided the motivation, encouragement, guidance and advice which have prepared me for the challenges of future life. I was fortunately exposed to industrial applications while working in the Intelligent Systems Laboratory. This invaluable experience has allowed me to maintain a balance between theory and practice leading to realistic solutions. MidAmerican Energy Company. The energy experts from company and Iowa Energy Center have extended invaluable information for this research. I thank all the members of the Intelligent Systems Laboratory who have worked with me and provided advice, reviews and suggestions. Special thanks to my colleagues: Dr. Zhe Song, who worked with me to solve challenging problems in wind energy domain; Wenyan Li, who shared her research experience with me; Mingyang Li, who enhanced my research capability through frequent communication and collaboration; and Robert A. Hamel, who discussed with me in wind energy topics and provided access to industrial data. And finally, and most importantly, I would like to express my sincere gratitude to my parents, who solidly supported me in my academic pursuit. v ABSTRACT Vibrations of a wind turbine have a negative impact on its performance and therefore approaches to effectively control turbines 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 …