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Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses

Author

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  • Kevin Leahy

    (School of Engineering, University College Cork, Cork T12 K8AF, Ireland
    Science Foundation Ireland (SFI) MaREI Centre, Environmental Research Institute, University College Cork, Cork P43 C573, Ireland)

  • Colm Gallagher

    (School of Engineering, University College Cork, Cork T12 K8AF, Ireland
    Science Foundation Ireland (SFI) MaREI Centre, Environmental Research Institute, University College Cork, Cork P43 C573, Ireland)

  • Peter O’Donovan

    (School of Engineering, University College Cork, Cork T12 K8AF, Ireland)

  • Dominic T. J. O’Sullivan

    (School of Engineering, University College Cork, Cork T12 K8AF, Ireland)

Abstract

In order to remain competitive, wind turbines must be reliable machines with efficient and effective maintenance strategies. However, thus far, wind turbine reliability information has been closely guarded by the original equipment manufacturers (OEMs), and turbine reliability studies often rely on data that are not always in a usable or consistent format. In addition, issues with turbine maintenance logs and alarm system data can make it hard to identify historical periods of faulty operation. This means that building new and effective data-driven condition monitoring techniques and methods can be challenging, especially those that rely on supervisory control and data acquisition (SCADA) system data. Such data are rarely standardised, resulting in challenges for researchers in contextualising these data. This work aims to summarise some of the issues seen in previous studies, highlighting the common problems seen by researchers working in the areas of condition monitoring and reliability analysis. Standards and policy initiatives that aim to alleviate some of these problems are given, and a summary of their recommendations is presented. The main finding from this work is that industry would benefit hugely from unified standards for turbine taxonomies, alarm codes, SCADA operational data and maintenance and fault reporting.

Suggested Citation

  • Kevin Leahy & Colm Gallagher & Peter O’Donovan & Dominic T. J. O’Sullivan, 2019. "Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses," Energies, MDPI, vol. 12(2), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:201-:d:196145
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    References listed on IDEAS

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    3. Cevasco, D. & Koukoura, S. & Kolios, A.J., 2021. "Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
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    5. Tongke Yuan & Zhifeng Sun & Shihao Ma, 2019. "Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection," Energies, MDPI, vol. 12(22), pages 1-20, November.
    6. Juan Izquierdo & Adolfo Crespo Márquez & Jone Uribetxebarria & Asier Erguido, 2019. "Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance," Energies, MDPI, vol. 12(11), pages 1-17, May.
    7. Urmeneta, Jon & Izquierdo, Juan & Leturiondo, Urko, 2023. "A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 281-292.
    8. 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.
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    10. Xiangqing Yin & Yi Liu & Li Yang & Wenchao Gao, 2022. "Abnormal Data Cleaning Method for Wind Turbines Based on Constrained Curve Fitting," Energies, MDPI, vol. 15(17), pages 1-22, August.
    11. Gonzalo Gil & Aitor Arnaiz & Mariví Higuero & Francisco Javier Diez & Eduardo Jacob, 2022. "Context-Aware Policy Analysis for Distributed Usage Control," Energies, MDPI, vol. 15(19), pages 1-25, September.
    12. Fernanda Moura Quintão Silva & Menaouar Berrehil El Kattel & Igor Amariz Pires & Thales Alexandre Carvalho Maia, 2022. "Development of a Supervisory System Using Open-Source for a Power Micro-Grid Composed of a Photovoltaic (PV) Plant Connected to a Battery Energy Storage System and Loads," Energies, MDPI, vol. 15(22), pages 1-22, November.
    13. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
    14. Sarah Barber & Luiz Andre Moyses Lima & Yoshiaki Sakagami & Julian Quick & Effi Latiffianti & Yichao Liu & Riccardo Ferrari & Simon Letzgus & Xujie Zhang & Florian Hammer, 2022. "Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study," Energies, MDPI, vol. 15(15), pages 1-32, August.
    15. Rad Haghi & Cassidy Stagg & Curran Crawford, 2024. "Wind Turbine Damage Equivalent Load Assessment Using Gaussian Process Regression Combining Measurement and Synthetic Data," Energies, MDPI, vol. 17(2), pages 1-24, January.
    16. 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).
    17. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Methodologies in Smart Grid-Integrated Doubly Fed Induction Generator Design Optimization and Reliability Assessment: A Review," Energies, MDPI, vol. 15(19), pages 1-39, September.

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