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Title Wind turbine dynamics identification using gaussian process machine learning / Edward Hart.
Name Hart, Edward. .
Abstract Gaussian process machine learning is chosen as the regression approach best suited for application in this problem. However, a review of existing batched Gaussian process theory results in the identification of gaps in the current knowledge base which render existing methods unsuitable. A new approach to batched Gaussian process regression is therefore developed, Sufficient-Subset Gaussian process iteration, which addresses the questions for which existing theories come up short. In the process of developing this new method fundamental contributions have been made to the areas of Gaussian process polynomial regression and sparse Gaussian process approximation theory.
Abstract Sufficient-Subset Gaussian process iteration is applied to both simulated and real turbine data and shown to be able to identify the sought dynamics to within a 3% error threshold. Additionally, a related regression formulation corresponding to maximum efficiency tracking is shown to present a potential method for turbine monitoring and fault detection.
Abstract The determination of sought dynamic information is formulated as a regression problem involving data available to a wind turbine controller. The nature of the dynamics identification problem is shown to necessitate a regression method which is able to process data in batches, updating predictions as new data becomes available.
Abstract Wind turbine controllers require dynamic information about the turbine for design and operation purposes. These dynamics are currently determined from simulation models during the turbine design stages. Hence, the dynamics for a given operational turbine will not be identical to those assumed by the controller due to manufacturing and construction variations. Furthermore, turbine dynamics are known to change over time due to environmental effects such as blade erosion. There are currently no known methods by which such information can be determined for an operational turbine. This thesis presents such a method.
Publication date 2018
Name Feuchtwang, Julian, degree supervisor.
Name Leithead, Bill, degree supervisor.
Name University of Edinburgh.
Name University of Oxford.
Name University of Strathclyde. Department of Electronic and Electrical Engineering.
Name University of Strathclyde. Department of Electronic and Electrical Engineering. Wind & Marine Energy Systems Centre for Doctoral Training.
Thesis note Thesis PhD University of Strathclyde 2018 T15071
System Number 000005488

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