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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:760314 |
| Date | January 2018 |
| Creators | Crespo, Manuel |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/8420/ |
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