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Advanced Methods for Wind Turbine Performance Analysis Based on SCADA Data and CFD Simulations

Author

Listed:
  • Francesco Castellani

    (Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy)

  • Ravi Pandit

    (Centre for Life-Cycle Engineering and Management (CLEM), School of Aerospace Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK)

  • Francesco Natili

    (Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy)

  • Francesca Belcastro

    (FERA Srl, Piazza Cavour 7, 20121 Milan, Italy)

  • Davide Astolfi

    (Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy)

Abstract

Deep comprehension of wind farm performance is a complicated task due to the multivariate dependence of wind turbine power on environmental variables and working parameters and to the intrinsic limitations in the quality of SCADA-collected measurements. Given this, the objective of this study is to propose an integrated approach based on SCADA data and Computational Fluid Dynamics simulations, which is aimed at wind farm performance analysis. The selected test case is a wind farm situated in southern Italy, where two wind turbines had an apparent underperformance. The concept of a space–time comparison at the wind farm level is leveraged by analyzing the operation curves of the wind turbines and by comparing the simulated average wind field against the measured one, where each wind turbine is treated like a virtual meteorological mast. The employed formulation for the CFD simulations is Reynolds-Average Navier–Stokes (RANS). In this work, it is shown that, based on the above approach, it has been possible to identify an anemometer bias at a wind turbine, which has subsequently been fixed. The results of this work affirm that a deep comprehension of wind farm performance requires a non-trivial space–time comparison, of which CFD simulations can be a fundamental part.

Suggested Citation

  • Francesco Castellani & Ravi Pandit & Francesco Natili & Francesca Belcastro & Davide Astolfi, 2023. "Advanced Methods for Wind Turbine Performance Analysis Based on SCADA Data and CFD Simulations," Energies, MDPI, vol. 16(3), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1081-:d:1040355
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    References listed on IDEAS

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    1. Ding, Yu & Kumar, Nitesh & Prakash, Abhinav & Kio, Adaiyibo E. & Liu, Xin & Liu, Lei & Li, Qingchang, 2021. "A case study of space-time performance comparison of wind turbines on a wind farm," Renewable Energy, Elsevier, vol. 171(C), pages 735-746.
    2. Davide Astolfi & Ravi Pandit & Ludovico Terzi & Andrea Lombardi, 2022. "Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis," Energies, MDPI, vol. 15(15), pages 1-17, July.
    3. Davide Astolfi & Francesco Castellani & Matteo Becchetti & Andrea Lombardi & Ludovico Terzi, 2020. "Wind Turbine Systematic Yaw Error: Operation Data Analysis Techniques for Detecting It and Assessing Its Performance Impact," Energies, MDPI, vol. 13(9), pages 1-17, May.
    4. Yuan Song & Insu Paek, 2020. "Prediction and Validation of the Annual Energy Production of a Wind Turbine Using WindSim and a Dynamic Wind Turbine Model," Energies, MDPI, vol. 13(24), pages 1-15, December.
    5. Daniel Tabas & Jiannong Fang & Fernando Porté-Agel, 2019. "Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics," Energies, MDPI, vol. 12(7), pages 1-12, April.
    6. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2020. "Analysis of Wind Turbine Aging through Operation Curves," Energies, MDPI, vol. 13(21), pages 1-21, October.
    7. Hwangbo, Hoon & Ding, Yu & Eisele, Oliver & Weinzierl, Guido & Lang, Ulrich & Pechlivanoglou, Georgios, 2017. "Quantifying the effect of vortex generator installation on wind power production: An academia-industry case study," Renewable Energy, Elsevier, vol. 113(C), pages 1589-1597.
    8. Davide Astolfi & Ravi Pandit & Linyue Gao & Jiarong Hong, 2022. "Individuation of Wind Turbine Systematic Yaw Error through SCADA Data," Energies, MDPI, vol. 15(21), pages 1-5, November.
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    Cited by:

    1. Ravi Kumar Pandit & Davide Astolfi & Isidro Durazo Cardenas, 2023. "A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines," Energies, MDPI, vol. 16(4), pages 1-17, February.
    2. Davide Astolfi & Silvia Iuliano & Antony Vasile & Marco Pasetti & Salvatore Dello Iacono & Alfredo Vaccaro, 2024. "Wind Turbine Static Errors Related to Yaw, Pitch or Anemometer Apparatus: Guidelines for the Diagnosis and Related Performance Assessment," Energies, MDPI, vol. 17(24), pages 1-34, December.

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