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Improving wind turbine power curve monitoring with standardisation

Author

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  • Helbing, Georg
  • Ritter, Matthias

Abstract

Against the background of increasing cost pressure, condition monitoring is becoming increasingly relevant to the wind energy industry. The present study examines the role of wind turbulence and the non-constant variance of residuals in power curve monitoring. Power curve monitoring methods are classified and compared by means of Monte Carlo simulations. It is found that adjusting for the non-constant variance of residuals using standardisation may considerably improve the performance of control charts, no matter what method is used to generate them. Additionally, turbulence is found to be an important factor, and including it may further increase the performance of control charts.

Suggested Citation

  • Helbing, Georg & Ritter, Matthias, 2020. "Improving wind turbine power curve monitoring with standardisation," Renewable Energy, Elsevier, vol. 145(C), pages 1040-1048.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:1040-1048
    DOI: 10.1016/j.renene.2019.06.112
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    Citations

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    Cited by:

    1. Davide Astolfi & Francesco Castellani & Andrea Lombardi & Ludovico Terzi, 2021. "Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring," Energies, MDPI, vol. 14(4), pages 1-18, February.
    2. Wang, Bohan & Deng, Ziwei & Zhang, Baocheng, 2022. "Simulation of a novel wind–wave hybrid power generation system with hydraulic transmission," Energy, Elsevier, vol. 238(PB).
    3. Pengfei Zhang & Zuoxia Xing & Shanshan Guo & Mingyang Chen & Qingqi Zhao, 2022. "A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging," Energies, MDPI, vol. 15(13), pages 1-15, July.
    4. Bórawski, Piotr & Bełdycka-Bórawska, Aneta & Jankowski, Krzysztof Jóżef & Dubis, Bogdan & Dunn, James W., 2020. "Development of wind energy market in the European Union," Renewable Energy, Elsevier, vol. 161(C), pages 691-700.
    5. Miguel Á. Rodríguez-López & Emilio Cerdá & Pablo del Rio, 2020. "Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation," Energies, MDPI, vol. 13(18), pages 1-21, September.
    6. Wang, Peng & Li, Yanting & Zhang, Guangyao, 2023. "Probabilistic power curve estimation based on meteorological factors and density LSTM," Energy, Elsevier, vol. 269(C).
    7. Han, Shuang & Qiao, Yanhui & Yan, Ping & Yan, Jie & Liu, Yongqian & Li, Li, 2020. "Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles," Renewable Energy, Elsevier, vol. 157(C), pages 190-203.

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