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Article

MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms

by 1,‡, 2,*,‡, 3,†,‡, 4,†,‡ and 5,‡
1
University of the Basque Country (UPV/EHU), Otaola 29, 20600 Eibar, Spain
2
Department of NE and Fluid Mechanics, University of the Basque Country (UPV/EHU), Otaola 29, 20600 Eibar, Spain
3
Department of NE and Fluid Mechanics, University of the Basque Country (UPV/EHU), Alda, Urkijo, 48013 Bilbao, Spain
4
Department of Applied Physics II, University of the Basque Country (UPV/EHU), B. Sarriena s/n, 48940 Leioa, Spain
5
Maxwind Technology, Portuetxe 83, 20018 Donostia, Spain
*
Author to whom correspondence should be addressed.
BEGIK, Joint Research Unit (UPV/EHU-IEO) Plentziako Itsas Estazioa (PIE), University of the Basque Country (UPV/EHU), Areatza Hiribidea 47, 48620 Plentzia, Spain.
These authors contributed equally to this work.
Energies 2019, 12(1), 28; https://doi.org/10.3390/en12010028
Received: 24 November 2018 / Revised: 17 December 2018 / Accepted: 18 December 2018 / Published: 22 December 2018
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
A novel multi-criteria methodology for the identification of defective anemometers is shown in this paper with a benchmarking approach: it is called MIDAS: multi-technique identification of defective anemometers. The identification of wrong wind data as provided by malfunctioning devices is very important, because the actual power curve of a wind turbine is conditioned by the quality of its anemometer measurements. Here, we present a novel method applied for the first time to anemometers’ data based on the kernel probability density function and the recent reanalysis ERA5. This estimation improves classical unidimensional methods such as the Kolmogorov–Smirnov test, and the use of the global ERA5’s wind data as the first benchmarking reference establishes a general method that can be used anywhere. Therefore, adopting ERA5 as the reference, this method is applied bi-dimensionally for the zonal and meridional components of wind, thus checking both components at the same time. This technique allows the identification of defective anemometers, as well as clear identification of the group of anemometers that works properly. After that, other verification techniques were used versus the faultless anemometers (Taylor diagrams, running correlation and R M S E , and principal component analysis), and coherent results were obtained for all statistical techniques with respect to the multidimensional method. The developed methodology combines the use of this set of techniques and was able to identify the defective anemometers in a wind farm with 10 anemometers located in Northern Europe in a terrain with forests and woodlands. Nevertheless, this methodology is general-purpose and not site-dependent, and in the future, its performance will be studied in other types of terrain and wind farms. View Full-Text
Keywords: wind turbine; anemometer; kernel-based multidimensional probability density function; ERA5 reanalysis wind turbine; anemometer; kernel-based multidimensional probability density function; ERA5 reanalysis
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MDPI and ACS Style

Rabanal, A.; Ulazia, A.; Ibarra-Berastegi, G.; Sáenz, J.; Elosegui, U. MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms. Energies 2019, 12, 28. https://doi.org/10.3390/en12010028

AMA Style

Rabanal A, Ulazia A, Ibarra-Berastegi G, Sáenz J, Elosegui U. MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms. Energies. 2019; 12(1):28. https://doi.org/10.3390/en12010028

Chicago/Turabian Style

Rabanal, Arkaitz, Alain Ulazia, Gabriel Ibarra-Berastegi, Jon Sáenz, and Unai Elosegui. 2019. "MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms" Energies 12, no. 1: 28. https://doi.org/10.3390/en12010028

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