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Power Optimization for Wind Turbines Based on Stacking Model and Pitch Angle Adjustment

Author

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  • Zhikun Luo

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Zhifeng Sun

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Fengli Ma

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yihan Qin

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Shihao Ma

    (Wuzhong Baita Wind Power Corporation Limited, Wuzhong 751100, China)

Abstract

As we know, power optimization for wind turbines has great significance in the area of wind power generation, which means to make use of wind resources more efficiently. Especially nowadays, wind power generation has become more and more important. Generally speaking, many parameters could be optimized to enhance power output, including blade pitch angle, which is usually ignored. In this article, a stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBOOST) and Light Gradient Boosting Machine (LGBM) is trained based on historical data exported from the Supervisory Control and Data Acquisition (SCADA) system for output power prediction. Then, we carry out power optimization through pitch angle adjustment based on the obtained prediction model. Our research results indicate that power output could be enhanced by adjusting pitch angle appropriately.

Suggested Citation

  • Zhikun Luo & Zhifeng Sun & Fengli Ma & Yihan Qin & Shihao Ma, 2020. "Power Optimization for Wind Turbines Based on Stacking Model and Pitch Angle Adjustment," Energies, MDPI, vol. 13(16), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4158-:d:397717
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    References listed on IDEAS

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    1. Tongke Yuan & Zhifeng Sun & Shihao Ma, 2019. "Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection," Energies, MDPI, vol. 12(22), pages 1-20, November.
    2. Mérida, Jován & Aguilar, Luis T. & Dávila, Jorge, 2014. "Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization," Renewable Energy, Elsevier, vol. 71(C), pages 715-728.
    3. Dai, Kaoshan & Bergot, Anthony & Liang, Chao & Xiang, Wei-Ning & Huang, Zhenhua, 2015. "Environmental issues associated with wind energy – A review," Renewable Energy, Elsevier, vol. 75(C), pages 911-921.
    4. Song, M.X. & Chen, K. & Zhang, X. & Wang, J., 2015. "The lazy greedy algorithm for power optimization of wind turbine positioning on complex terrain," Energy, Elsevier, vol. 80(C), pages 567-574.
    5. Fan, Zhixin & Zhu, Caichao, 2019. "The optimization and the application for the wind turbine power-wind speed curve," Renewable Energy, Elsevier, vol. 140(C), pages 52-61.
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    Cited by:

    1. Zbigniew Skibko & Magdalena Tymińska & Wacław Romaniuk & Andrzej Borusiewicz, 2021. "Impact of the Wind Turbine on the Parameters of the Electricity Supply to an Agricultural Farm," Sustainability, MDPI, vol. 13(13), pages 1-15, June.
    2. Jiyuan Zhang & Qihong Feng & Xianmin Zhang & Qiujia Hu & Jiaosheng Yang & Ning Wang, 2020. "A Novel Data-Driven Method to Estimate Methane Adsorption Isotherm on Coals Using the Gradient Boosting Decision Tree: A Case Study in the Qinshui Basin, China," Energies, MDPI, vol. 13(20), pages 1-21, October.
    3. Ming-Fa Tsai & Chung-Shi Tseng & Bor-Yuh Lin, 2020. "Phase Voltage-Oriented Control of a PMSG Wind Generator for Unity Power Factor Correction," Energies, MDPI, vol. 13(21), pages 1-22, October.
    4. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review," Energies, MDPI, vol. 15(17), pages 1-56, September.

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