A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Classification of Wind Turbine Blade Performance State Through Statistical Methods
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.
Classification of Wind Turbine Blade Performance State Through Statistical Methods
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.
Classification of Wind Turbine Blade Performance State Through Statistical Methods
Shen, Jones (author)
2019-03-10
Electronic Theses and Dissertations
Theses
Electronic Resource
English
DDC:
690
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