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Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

Author

Listed:
  • Francisco Bilendo

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Angela Meyer

    (Department of Engineering and Information Technology, Bern University of Applied Sciences, 2501 Biel, Switzerland)

  • Hamed Badihi

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Ningyun Lu

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Philippe Cambron

    (Department of Wind Energy Research and Development (R&D), Power Factors, Montreal, QC J4Z 1A7, Canada)

  • Bin Jiang

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

Abstract

In the wind energy industry, the power curve represents the relationship between the “wind speed” at the hub height and the corresponding “active power” to be generated. It is the most versatile condition indicator and of vital importance in several key applications, such as wind turbine selection, capacity factor estimation, wind energy assessment and forecasting, and condition monitoring, among others. Ensuring an effective implementation of the aforementioned applications mostly requires a modeling technique that best approximates the normal properties of an optimal wind turbines operation in a particular wind farm. This challenge has drawn the attention of wind farm operators and researchers towards the “state of the art” in wind energy technology. This paper provides an exhaustive and updated review on power curve based applications, the most common anomaly and fault types including their root-causes, along with data preprocessing and correction schemes (i.e., filtering, clustering, isolation, and others), and modeling techniques (i.e., parametric and non-parametric) which cover a wide range of algorithms. More than 100 references, for the most part selected from recently published journal articles, were carefully compiled to properly assess the past, present, and future research directions in this active domain.

Suggested Citation

  • Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:180-:d:1013504
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    References listed on IDEAS

    as
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    Cited by:

    1. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.

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