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Wind Farm and Resource Datasets: A Comprehensive Survey and Overview

Author

Listed:
  • Diogo Menezes

    (Polythecnic Institute of Coimbra—ISEC, 3030-199 Coimbra, Portugal)

  • Mateus Mendes

    (Polythecnic Institute of Coimbra—ISEC, 3030-199 Coimbra, Portugal
    Institute of Systems and Robotics, University of Coimbra—ISR, DEEC, 3030-290 Coimbra, Portugal)

  • Jorge Alexandre Almeida

    (Polythecnic Institute of Coimbra—ISEC, 3030-199 Coimbra, Portugal
    Electromechatronic Systems Research Centre, University Beira Interior, 6201-001 Covilhã, Portugal)

  • Torres Farinha

    (Polythecnic Institute of Coimbra—ISEC, 3030-199 Coimbra, Portugal
    Centre for Mechanical Engineering, Materials and Processes, 3030-788 Coimbra, Portugal)

Abstract

The use of clean and renewable energy sources is increasingly important, for economic and environmental reasons. Wind plays a key role among renewable energy sources. Hence, the location, monitoring and maintenance of wind turbines are areas that have received more and more attention in recent years. The paper presents a survey of datasets of wind resources, wind farm installed capacity and wind farm operation, which contain generous amounts of data. Those datasets are important tools, freely available for analysis of wind resources and study of the performance of wind turbines. A short analysis of one of the datasets is also presented, identifying different operational regions, and the ones more likely to aggregate failures. Principal Component Analysis (PCA) is used to study wind turbines’ behavior.

Suggested Citation

  • Diogo Menezes & Mateus Mendes & Jorge Alexandre Almeida & Torres Farinha, 2020. "Wind Farm and Resource Datasets: A Comprehensive Survey and Overview," Energies, MDPI, vol. 13(18), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4702-:d:411100
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    References listed on IDEAS

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    Cited by:

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    2. Woochul Nam & Ki-Yong Oh, 2020. "Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment," Mathematics, MDPI, vol. 8(10), pages 1-20, October.
    3. 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).
    4. Zhao Song & Christoph M. Hackl & Abhinav Anand & Andre Thommessen & Jonas Petzschmann & Omar Kamel & Robert Braunbehrens & Anton Kaifel & Christian Roos & Stefan Hauptmann, 2023. "Digital Twins for the Future Power System: An Overview and a Future Perspective," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    5. Ali M. H. A. Khajah & Simon P. Philbin, 2022. "Techno-Economic Analysis and Modelling of the Feasibility of Wind Energy in Kuwait," Clean Technol., MDPI, vol. 4(1), pages 1-21, January.
    6. Hesong Cui & Xueping Li & Gongping Wu & Yawei Song & Xiao Liu & Derong Luo, 2021. "MPC Based Coordinated Active and Reactive Power Control Strategy of DFIG Wind Farm with Distributed ESSs," Energies, MDPI, vol. 14(13), pages 1-19, June.

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