IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i5p1010-d1342907.html
   My bibliography  Save this article

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Author

Listed:
  • Rogerio Adriano da Fonseca Santiago

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Natasha Benjamim Barbosa

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Henrique Gomes Mergulhão

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Tassio Farias de Carvalho

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Alex Alisson Bandeira Santos

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil
    Instituto de Ciência, Inovação e Tecnologia em Energias Renováveis do Estado da Bahia—INCITERE, Salvador 40210-910, BA, Brazil)

  • Ricardo Cerqueira Medrado

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Jose Bione de Melo Filho

    (Eletrobras Chesf, R. Delmiro Gouveia, 333, Recife 41650-010, BA, Brazil)

  • Oberdan Rocha Pinheiro

    (Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Erick Giovani Sperandio Nascimento

    (Surrey Institute for People-Centred AI, School of Computer Science and Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
    Stricto Sensu Department, SENAI CIMATEC, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

Abstract

Wind energy has achieved a leading position among renewable energies. The global installed capacity in 2022 was 906 GW of power, with a growth of 8.4% compared to the same period in the previous year. The forecast is that the barrier of 1,000,000 MW of installed wind capacity in the world will be exceeded in July 2023, according to data from the World Association of Wind Energy. In order to support the expected growth in the wind sector, maintenance strategies for wind turbines must provide the reliability and availability necessary to achieve these goals. The usual maintenance procedures may present difficulties in keeping up with the expansion of this energy source. The objective of this work was to carry out a systematic review of the literature focused on research on the predictive and prescriptive maintenance of wind turbines based on the implementation of data-oriented models with the use of artificial intelligence tools. Deep machine learning models involving the detection, diagnosis, and prognosis of failures in this equipment were addressed.

Suggested Citation

  • Rogerio Adriano da Fonseca Santiago & Natasha Benjamim Barbosa & Henrique Gomes Mergulhão & Tassio Farias de Carvalho & Alex Alisson Bandeira Santos & Ricardo Cerqueira Medrado & Jose Bione de Melo Fi, 2024. "Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis," Energies, MDPI, vol. 17(5), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1010-:d:1342907
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/5/1010/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/5/1010/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    2. Zhao, Hongshan & Liu, Huihai & Hu, Wenjing & Yan, Xihui, 2018. "Anomaly detection and fault analysis of wind turbine components based on deep learning network," Renewable Energy, Elsevier, vol. 127(C), pages 825-834.
    3. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    Full references (including those not matched with items on IDEAS)

    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. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
    2. Jia Tian & Xingqin Zhang & Shuangqing Zheng & Zhiyong Liu & Changshu Zhan, 2024. "Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems," Mathematics, MDPI, vol. 12(9), pages 1-25, April.
    3. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
    4. Feng, Chenlong & Liu, Chao & Jiang, Dongxiang, 2023. "Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning," Renewable Energy, Elsevier, vol. 206(C), pages 309-323.
    5. Zhang, Chen & Yang, Tao, 2021. "Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ," Renewable Energy, Elsevier, vol. 164(C), pages 1540-1549.
    6. Wang, Bo & Wang, Jianda & Dong, Kangyin & Nepal, Rabindra, 2024. "How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society," Energy Policy, Elsevier, vol. 186(C).
    7. Xu, Qifa & Fan, Zhenhua & Jia, Weiyin & Jiang, Cuixia, 2020. "Fault detection of wind turbines via multivariate process monitoring based on vine copulas," Renewable Energy, Elsevier, vol. 161(C), pages 939-955.
    8. Yang, Weifei & Xiao, Changlai & Zhang, Zhihao & Liang, Xiujuan, 2022. "Identification of the formation temperature field of the southern Songliao Basin, China based on a deep belief network," Renewable Energy, Elsevier, vol. 182(C), pages 32-42.
    9. Meng Li & Shuangxin Wang, 2019. "Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks," Energies, MDPI, vol. 12(17), pages 1-20, August.
    10. Jastrzebska, Agnieszka & Morales Hernández, Alejandro & Nápoles, Gonzalo & Salgueiro, Yamisleydi & Vanhoof, Koen, 2022. "Measuring wind turbine health using fuzzy-concept-based drifting models," Renewable Energy, Elsevier, vol. 190(C), pages 730-740.
    11. Sagir, Emrah & Alipour, Siamak, 2021. "Photofermentative hydrogen production by immobilized photosynthetic bacteria: Current perspectives and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    12. 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.
    13. Matthew Boeding & Kelly Boswell & Michael Hempel & Hamid Sharif & Juan Lopez & Kalyan Perumalla, 2022. "Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid," Energies, MDPI, vol. 15(22), pages 1-22, November.
    14. Paweł Knes & Phong B. Dao, 2024. "Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach," Energies, MDPI, vol. 17(20), pages 1-21, October.
    15. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
    16. Zhang, Chen & Gao, Wei & Yang, Tao & Guo, Sheng, 2019. "Opportunistic maintenance strategy for wind turbines considering weather conditions and spare parts inventory management," Renewable Energy, Elsevier, vol. 133(C), pages 703-711.
    17. Muhammad Amin Durrani & Iftikhar Ahmad & Manabu Kano & Shinji Hasebe, 2018. "An Artificial Intelligence Method for Energy Efficient Operation of Crude Distillation Units under Uncertain Feed Composition," Energies, MDPI, vol. 11(11), pages 1-12, November.
    18. Yueqiang Xu & Petri Ahokangas & Jean-Nicolas Louis & Eva Pongrácz, 2019. "Electricity Market Empowered by Artificial Intelligence: A Platform Approach," Energies, MDPI, vol. 12(21), pages 1-21, October.
    19. Leal Filho, Walter & Wall, Tony & Rui Mucova, Serafino Afonso & Nagy, Gustavo J. & Balogun, Abdul-Lateef & Luetz, Johannes M. & Ng, Artie W. & Kovaleva, Marina & Safiul Azam, Fardous Mohammad & Alves,, 2022. "Deploying artificial intelligence for climate change adaptation," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    20. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.

    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:17:y:2024:i:5:p:1010-:d:1342907. 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.