IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2018i1p28-d192636.html
   My bibliography  Save this article

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

Author

Listed:
  • Arkaitz Rabanal

    (University of the Basque Country (UPV/EHU), Otaola 29, 20600 Eibar, Spain
    These authors contributed equally to this work.)

  • Alain Ulazia

    (Department of NE and Fluid Mechanics, University of the Basque Country (UPV/EHU), Otaola 29, 20600 Eibar, Spain
    These authors contributed equally to this work.)

  • Gabriel Ibarra-Berastegi

    (Department of NE and Fluid Mechanics, University of the Basque Country (UPV/EHU), Alda, Urkijo, 48013 Bilbao, Spain
    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.)

  • Jon Sáenz

    (Department of Applied Physics II, University of the Basque Country (UPV/EHU), B. Sarriena s/n, 48940 Leioa, Spain
    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.)

  • Unai Elosegui

    (Maxwind Technology, Portuetxe 83, 20018 Donostia, Spain
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Arkaitz Rabanal & Alain Ulazia & Gabriel Ibarra-Berastegi & Jon Sáenz & Unai Elosegui, 2018. "MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms," Energies, MDPI, vol. 12(1), pages 1-19, December.
  • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:28-:d:192636
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/1/28/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/1/28/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Unai Elosegui & Igor Egana & Alain Ulazia & Gabriel Ibarra-Berastegi, 2018. "Pitch Angle Misalignment Correction Based on Benchmarking and Laser Scanner Measurement in Wind Farms," Energies, MDPI, vol. 11(12), pages 1-20, December.
    2. Dai, Juchuan & Yang, Xin & Hu, Wei & Wen, Li & Tan, Yayi, 2018. "Effect investigation of yaw on wind turbine performance based on SCADA data," Energy, Elsevier, vol. 149(C), pages 684-696.
    3. Elena Roibas-Millan & Javier Cubas & Santiago Pindado, 2017. "Studies on Cup Anemometer Performances Carried out at IDR/UPM Institute. Past and Present Research," Energies, MDPI, vol. 10(11), pages 1-17, November.
    4. Alain Ulazia & Markel Penalba & Arkaitz Rabanal & Gabriel Ibarra-Berastegi & John Ringwood & Jon Sáenz, 2018. "Historical Evolution of the Wave Resource and Energy Production off the Chilean Coast over the 20th Century," Energies, MDPI, vol. 11(9), pages 1-23, August.
    5. Han, Xingxing & Liu, Deyou & Xu, Chang & Shen, Wen Zhong, 2018. "Atmospheric stability and topography effects on wind turbine performance and wake properties in complex terrain," Renewable Energy, Elsevier, vol. 126(C), pages 640-651.
    6. Olauson, Jon, 2018. "ERA5: The new champion of wind power modelling?," Renewable Energy, Elsevier, vol. 126(C), pages 322-331.
    7. Penalba, Markel & Ulazia, Alain & Ibarra-Berastegui, Gabriel & Ringwood, John & Sáenz, Jon, 2018. "Wave energy resource variation off the west coast of Ireland and its impact on realistic wave energy converters’ power absorption," Applied Energy, Elsevier, vol. 224(C), pages 205-219.
    8. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    9. Ulazia, Alain & Penalba, Markel & Ibarra-Berastegui, Gabriel & Ringwood, John & Saénz, Jon, 2017. "Wave energy trends over the Bay of Biscay and the consequences for wave energy converters," Energy, Elsevier, vol. 141(C), pages 624-634.
    10. Santiago Pindado & Antonio Barrero-Gil & Alfredo Sanz, 2012. "Cup Anemometers’ Loss of Performance Due to Ageing Processes, and Its Effect on Annual Energy Production (AEP) Estimates," Energies, MDPI, vol. 5(5), pages 1-22, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Oscar Garcia & Alain Ulazia & Mario del Rio & Sheila Carreno-Madinabeitia & Andoni Gonzalez-Arceo, 2019. "An Energy Potential Estimation Methodology and Novel Prototype Design for Building-Integrated Wind Turbines," Energies, MDPI, vol. 12(10), pages 1-21, May.
    2. Shuting Wan & Kanru Cheng & Xiaoling Sheng & Xuan Wang, 2019. "Characteristic Analysis of DFIG Wind Turbine under Blade Mass Imbalance Fault in View of Wind Speed Spatiotemporal Distribution," Energies, MDPI, vol. 12(16), pages 1-14, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oscar Garcia & Alain Ulazia & Mario del Rio & Sheila Carreno-Madinabeitia & Andoni Gonzalez-Arceo, 2019. "An Energy Potential Estimation Methodology and Novel Prototype Design for Building-Integrated Wind Turbines," Energies, MDPI, vol. 12(10), pages 1-21, May.
    2. Ulazia, Alain & Saenz-Aguirre, Aitor & Ibarra-Berastegui, Gabriel & Sáenz, Jon & Carreno-Madinabeitia, Sheila & Esnaola, Ganix, 2023. "Performance variations of wave energy converters due to global long-term wave period change (1900–2010)," Energy, Elsevier, vol. 268(C).
    3. Ulazia, Alain & Esnaola, Ganix & Serras, Paula & Penalba, Markel, 2020. "On the impact of long-term wave trends on the geometry optimisation of oscillating water column wave energy converters," Energy, Elsevier, vol. 206(C).
    4. Ulazia, Alain & Penalba, Markel & Ibarra-Berastegui, Gabriel & Ringwood, John & Sáenz, Jon, 2019. "Reduction of the capture width of wave energy converters due to long-term seasonal wave energy trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    5. Penalba, Markel & Ulazia, Alain & Saénz, Jon & Ringwood, John V., 2020. "Impact of long-term resource variations on wave energy Farms: The Icelandic case," Energy, Elsevier, vol. 192(C).
    6. Sun, Peidong & Xu, Bin & Wang, Jichao, 2022. "Long-term trend analysis and wave energy assessment based on ERA5 wave reanalysis along the Chinese coastline," Applied Energy, Elsevier, vol. 324(C).
    7. Alain Ulazia & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia & Santos J. González-Rojí, 2019. "Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula," Sustainability, MDPI, vol. 11(13), pages 1-22, July.
    8. Carreno-Madinabeitia, Sheila & Ibarra-Berastegi, Gabriel & Sáenz, Jon & Ulazia, Alain, 2021. "Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010)," Energy, Elsevier, vol. 226(C).
    9. Joan Pau Sierra & Ricard Castrillo & Marc Mestres & César Mösso & Piero Lionello & Luigi Marzo, 2020. "Impact of Climate Change on Wave Energy Resource in the Mediterranean Coast of Morocco," Energies, MDPI, vol. 13(11), pages 1-19, June.
    10. Alain Ulazia & Ander Nafarrate & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia, 2019. "The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential," Energies, MDPI, vol. 12(13), pages 1-18, July.
    11. Alain Ulazia & Markel Penalba & Arkaitz Rabanal & Gabriel Ibarra-Berastegi & John Ringwood & Jon Sáenz, 2018. "Historical Evolution of the Wave Resource and Energy Production off the Chilean Coast over the 20th Century," Energies, MDPI, vol. 11(9), pages 1-23, August.
    12. Davide Astolfi & Francesco Castellani, 2019. "Wind Turbine Power Curve Upgrades: Part II," Energies, MDPI, vol. 12(8), pages 1-20, April.
    13. Orszaghova, J. & Lemoine, S. & Santo, H. & Taylor, P.H. & Kurniawan, A. & McGrath, N. & Zhao, W. & Cuttler, M.V.W., 2022. "Variability of wave power production of the M4 machine at two energetic open ocean locations: Off Albany, Western Australia and at EMEC, Orkney, UK," Renewable Energy, Elsevier, vol. 197(C), pages 417-431.
    14. Nuria Novas & Alfredo Alcayde & Isabel Robalo & Francisco Manzano-Agugliaro & Francisco G. Montoya, 2020. "Energies and Its Worldwide Research," Energies, MDPI, vol. 13(24), pages 1-41, December.
    15. Penalba, Markel & Aizpurua, Jose Ignacio & Martinez-Perurena, Ander & Iglesias, Gregorio, 2022. "A data-driven long-term metocean data forecasting approach for the design of marine renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    16. Shaobo Yang & Shanhua Duan & Linlin Fan & Chongwei Zheng & Xingfei Li & Hongyu Li & Jianjun Xu & Qiang Wang & Ming Feng, 2019. "10-Year Wind and Wave Energy Assessment in the North Indian Ocean," Energies, MDPI, vol. 12(20), pages 1-16, October.
    17. Ribeiro, A.S. & deCastro, M. & Costoya, X. & Rusu, Liliana & Dias, J.M. & Gomez-Gesteira, M., 2021. "A Delphi method to classify wave energy resource for the 21st century: Application to the NW Iberian Peninsula," Energy, Elsevier, vol. 235(C).
    18. Kerman López de Calle & Susana Ferreiro & Constantino Roldán-Paraponiaris & Alain Ulazia, 2019. "A Context-Aware Oil Debris-Based Health Indicator for Wind Turbine Gearbox Condition Monitoring," Energies, MDPI, vol. 12(17), pages 1-19, September.
    19. Pourali, Mahmoud & Kavianpour, Mohamad Reza & Kamranzad, Bahareh & Alizadeh, Mohamad Javad, 2023. "Future variability of wave energy in the Gulf of Oman using a high resolution CMIP6 climate model," Energy, Elsevier, vol. 262(PB).
    20. Gil Ruiz, Samuel Andrés & Barriga, Julio Eduardo Cañón & Martínez, J. Alejandro, 2021. "Wind power assessment in the Caribbean region of Colombia, using ten-minute wind observations and ERA5 data," Renewable Energy, Elsevier, vol. 172(C), pages 158-176.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:28-:d:192636. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.