Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760314
Title: Wind turbine monitoring using short-range Doppler radar
Author: Crespo, Manuel
ISNI:       0000 0004 7432 3048
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2018
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Abstract:
This thesis summarises the research done on the feasibility of detecting and automatically classifying wind turbine faults using a short-range radar. Two main areas are included in the thesis: the theoretical and experimental analysis of wind turbine blade radar signatures in the near-field and the classification of wind turbine structural faults using machine learning algorithms. In the former, a new theoretical framework has been developed which extends the current far-field models and includes a mathematical and experimental analysis of simple flat blades as well as complex curved blades. The latter area comprises the analysis of the experimental results obtained using faulty wind turbine blades and methods of classifying these faults. This last task has been done in time and frequency domains using, respectively, the signals Statistical Parameters and the Principal Component Analysis algorithm for features extraction. The classification bas been performed employing the k-Nearest Neighbours algorithm. Finally, an Artificial Neural Network has been used as a more powerful classification tool in both domains.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.760314  DOI: Not available
Keywords: TK Electrical engineering. Electronics Nuclear engineering
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