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

Arkaitz Rabanal, Alain Ulazia, Gabriel Ibarra-Berastegi, Jon Sáenz and Unai Elosegui
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Arkaitz Rabanal: University of the Basque Country (UPV/EHU), Otaola 29, 20600 Eibar, Spain
Alain Ulazia: Department of NE and Fluid Mechanics, University of the Basque Country (UPV/EHU), Otaola 29, 20600 Eibar, Spain
Gabriel Ibarra-Berastegi: Department of NE and Fluid Mechanics, University of the Basque Country (UPV/EHU), Alda, Urkijo, 48013 Bilbao, Spain
Jon Sáenz: Department of Applied Physics II, University of the Basque Country (UPV/EHU), B. Sarriena s/n, 48940 Leioa, Spain
Unai Elosegui: Maxwind Technology, Portuetxe 83, 20018 Donostia, Spain

Energies, 2018, vol. 12, issue 1, 1-19

Abstract: 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.

Keywords: wind turbine; anemometer; kernel-based multidimensional probability density function; ERA5 reanalysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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