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Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

Author

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  • Jorge Maldonado-Correa

    (Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain
    Faculty of Energy Nacional, University of Loja, Loja 110150, Ecuador)

  • Sergio Martín-Martínez

    (Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain)

  • Estefanía Artigao

    (Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain)

  • Emilio Gómez-Lázaro

    (Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain)

Abstract

Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.

Suggested Citation

  • Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3132-:d:372597
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    References listed on IDEAS

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    17. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    18. Silvio Simani & Saverio Farsoni & Paolo Castaldi, 2023. "RETRACTED: Supervisory Control and Data Acquisition for Fault Diagnosis of Wind Turbines via Deep Transfer Learning," Energies, MDPI, vol. 16(9), pages 1-22, April.

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