EXPLORING KEY VARIABLES IN WIND TURBINE POWER CURVE MODELING
A Thesis
by
DAVID MATTHEW PEREZ
Submitted to the Office of Graduate and Professional Studies of
Texas A&M University
in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE
Chair of Committee,
Yu Ding
Committee Members,
Li Zeng
Mladen Kezunovic
Head of Department,
Mark Lawley
August 2018
Major Subject: Industrial Engineering
Copyright 2018 David Matthew Perez
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ABSTRACT
Though substantial evidence has shown the importance of wind speed and direction
in modelling a wind turbine’s power curve, there remains uncertainty as to whether other
variables would improve modelling efforts and which specific variables those would be.
This present work expands upon prior research using the additive multiplicative kernel
(AMK) technique to explore the use of additional variables. The experimental
methodology involves arriving at power estimates by treating a year’s amount of wind
turbine data as a learning problem. Different combinations of variables are investigated
and compared in terms of error reduction on the testing set using root mean square error
and mean average error. Discussion on the best sets of variables combinations are
presented to gain insight as to why certain variables lead to greater error reduction and
whether they are likely to be included in different sets of data.
Two categories of variables emerge from the research. The first includes variables
that are typically recorded in field operations including time, turbulent intensity, and the
standard deviation of wind direction. Time and turbulent intensity are shown to offer
promising results. The next set includes variables that measure how much wind speed and
direction vary as height varies. This is especially of interest as turbine size has increased
substantially over recent years. In particular, the rotor equivalent wind speed neatly
captures the variation of wind speed and direction across the length of a turbine’s rotor in a
single value. Using this parameter with AMK leads to significant prediction error
reduction, making a strong case to include it in modeling the power curve. As will be
discussed, doing so proves to be a better alternative than current industry practice.
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ACKNOWLEDGEMENTS
I would like to thank my committee chair, Dr. Ding, and my committee members,
Dr. Zeng and Dr. Kezunovic for their guidance in making this work of research possible.
Dr. Ding’s support enabled me to fully focus on classes and research. Support also
thankfully came from the Industrial & Systems Engineering Department in my time as a
teaching assistant.
I owe a tremendous debt of gratitude to Texas A&M University for their generous
financial support in the form of the Graduate Diversity Fellowship. Without the fellowship,
it is doubtful if I would have been able to pursue a Master’s Degree, so I will be eternally
grateful to the University for giving me this opportunity of a lifetime.
