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Development of an enhanced parametric model for wind turbine power curve

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  • Taslimi-Renani, Ehsan
  • Modiri-Delshad, Mostafa
  • Elias, Mohamad Fathi Mohamad
  • Rahim, Nasrudin Abd.

Abstract

Modeling of wind turbine power curve is greatly important in performance monitoring of the turbine and also in forecasting the wind power generation. In this paper, an accurate parametric model called modified hyperbolic tangent (MHTan) is proposed to characterize power curve of the wind turbine. The paper also presents the development of both parametric and nonparametric models of wind turbine power curve. In addition, least square error (LSE) and maximum likelihood estimation (MLE) are employed to estimate vector parameter of the proposed model. Here, three evolutionary algorithms, namely, particle swarm optimization, Cuckoo search, and backtracking search algorithm aid LSE and MLE. The performance of all presented methods is evaluated by a real data collected from a wind farm in Iran as well as three statistically generated data sets. The results demonstrate the efficiency of the proposed model compared to some other existing parametric and nonparametric models.

Suggested Citation

  • Taslimi-Renani, Ehsan & Modiri-Delshad, Mostafa & Elias, Mohamad Fathi Mohamad & Rahim, Nasrudin Abd., 2016. "Development of an enhanced parametric model for wind turbine power curve," Applied Energy, Elsevier, vol. 177(C), pages 544-552.
    • Handle: RePEc:eee:appene:v:177:y:2016:i:c:p:544-552
    DOI: 10.1016/j.apenergy.2016.05.124
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    8. Marino Marrocu & Luca Massidda, 2017. "A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast," Energies, MDPI, vol. 10(12), pages 1-14, November.
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    11. Mingzhe Zou & Sasa Z. Djokic, 2020. "A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements," Energies, MDPI, vol. 13(16), pages 1-30, August.
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    18. Wang, Yun & Duan, Xiaocong & Zou, Runmin & Zhang, Fan & Li, Yifen & Hu, Qinghua, 2023. "A novel data-driven deep learning approach for wind turbine power curve modeling," Energy, Elsevier, vol. 270(C).
    19. Yan, Jie & Zhang, Hao & Liu, Yongqian & Han, Shuang & Li, Li, 2019. "Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling," Applied Energy, Elsevier, vol. 239(C), pages 1356-1370.
    20. 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.
    21. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    22. Kies, Alexander & Schyska, Bruno U. & Bilousova, Mariia & El Sayed, Omar & Jurasz, Jakub & Stoecker, Horst, 2021. "Critical review of renewable generation datasets and their implications for European power system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    23. Gonzalez, Elena & Stephen, Bruce & Infield, David & Melero, Julio J., 2019. "Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study," Renewable Energy, Elsevier, vol. 131(C), pages 841-853.
    24. Liang, Guoyuan & Su, Yahao & Wu, Xinyu & Ma, Jiajun & Long, Huan & Song, Zhe, 2023. "Abnormal data cleaning for wind turbines by image segmentation based on active shape model and class uncertainty," Renewable Energy, Elsevier, vol. 216(C).
    25. Jianzhong Zhou & Na Sun & Benjun Jia & Tian Peng, 2018. "A Novel Decomposition-Optimization Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 11(7), pages 1-27, July.

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