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Identifying health status of wind turbines by using self organizing maps and interpretation-oriented post-processing tools

Author
Blanco, A.; Gibert, Karina; Marti, P.; Cusido, J.; Sole, J.
Type of activity
Journal article
Journal
Energies
Date of publication
2018-04-01
Volume
11
Number
4
First page
1
Last page
21
DOI
https://doi.org/10.3390/en11040723 Open in new window
Repository
http://hdl.handle.net/2117/123594 Open in new window
URL
http://www.mdpi.com/1996-1073/11/4/723 Open in new window
Abstract
Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25–35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expe...
Citation
Blanco, A., Gibert, Karina, Marti, P., Cusido, J., Sole, J. Identifying health status of wind turbines by using self organizing maps and interpretation-oriented post-processing tools. "Energies", 1 Abril 2018, vol. 11, núm. 4, p. 1-21.
Keywords
Supervisory Control and Data Acquisition(SCADA) data, clustering, data science, fault diagnosis, interpretation oriented tools, post- processing, renewable energy, self organizing maps (SOM), wind farms
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

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