Date of Award
3-10-2019
Publication Type
Master Thesis
Degree Name
M.A.Sc.
Department
Mechanical, Automotive, and Materials Engineering
Keywords
blades, condition monitoring, performance, SCADA, wind turbine
Supervisor
Rupp Carriveau
Supervisor
David Ting
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
As wind turbines continue to age, wind farm operators face the challenge of optimizing maintenance scheduling to reduce the associated operation and maintenance (O&M) costs. Wind farm operators typically use conservative maintenance scheduling in order to maximize the uptime of their wind turbines. In most cases however, maintenance may not be necessary and the components could operate for longer before repairs are required. This work presents three papers that collectively focus on providing potentially useful information to aid wind farm operators in making maintenance decisions. In the first paper, the utilization of Geographic Information Systems (GIS) to illustrate data trends across wind farms is introduced to better understand an operation’s signature performance characteristics. It is followed by a paper that presents an improved condition monitoring system for the wind turbine blades through the use of the principal component analysis (PCA). The final paper introduces another condition monitoring system using a k-means clustering algorithm to determine the performance state of wind turbine blades.
Recommended Citation
Shen, Jones, "Classification of Wind Turbine Blade Performance State Through Statistical Methods" (2019). Electronic Theses and Dissertations. 7657.
https://scholar.uwindsor.ca/etd/7657