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DOI:10.35833/MPCE.2022.000703
Characterization of Vertical Wind Speed Profiles Based on Ward’s Agglomerative Clustering Algorithm
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Author: M. C. Bueso1, A. Molina-García2, A. P. Ramallo-González3, A. Fernández-Guillamón4

Author Affiliation: 1. Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
2. Department of Automatics, Electrical Engineering and Electronic Technology, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
3. Department of Information and Communications Engineering, Computer Science Faculty, University of Murcia, 30100 Murcia, Spain
4. Universidad de Castilla La-Mancha, Campus Universitario, 02071 Albacete, Spain

Foundation:
This work was supported in part by the Ministry of Science and Innovation (Spain) (No. PID2021-126082OB-C22).
Abstract: Wind turbine blades have been constantly increasing since wind energy becomes a popular renewable energy source to generate electricity. Therefore, the wind sector requires a more efficient and representative characterization of vertical wind speed profiles to assess the potential for a wind power plant site. This paper proposes an alternative characterization of vertical wind speed profiles based on Ward’s agglomerative clustering algorithm, including both wind speed module and direction data. This approach gives a more accurate incoming wind speed variation around the rotor swept area, and subsequently, provides a more realistic and complete wind speed vector characterization for vertical profiles. Real wind data-base collected for 2018 in the Forschungsplattformen in Nord-und Ostsee (FINO) research platform is used to assess the methodology. A preliminary pre-processing stage is proposed to select the appropriated number of heights and remove missing or incomplete data. Finally, two locations and four heights are selected, and 561588 wind data are characterized. Results and discussion are also included in this paper. The methodology can be applied to other wind database and locations to characterize vertical wind speed profiles and identify the most likely wind data vector patterns.

Keywords:
Clustering algorithm ; wind speed ; data management ; power generation
Received:October 27, 2022               Online Time:2023/09/20
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