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Lorenz Wind Disturbance Model Based on Grey Generated Components

Author

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  • Yagang Zhang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
    Interdisciplinary Mathematics Institute, University of South Carolina, Columbia, SC 29208, USA)

  • Jingyun Yang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Kangcheng Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Yinding Wang

    (Interdisciplinary Mathematics Institute, University of South Carolina, Columbia, SC 29208, USA)

Abstract

In order to meet the needs of wind speed prediction in wind farms, we consider the influence of random atmospheric disturbances on wind variations. Considering a simplified fluid convection mode, a Lorenz system can be employed as an atmospheric disturbance model. Here Lorenz disturbance is defined as the European norm of the solutions of the Lorenz equation. Grey generating and accumulated generating models are employed to explore the relationship between wind speed and its related disturbance series. We conclude that a linear or quadric polynomial generating model are optimal through the verification of short-term wind speed prediction in the Sotavento wind farm. The new proposed model not only greatly improves the precision of short-term wind speed prediction, but also has great significance for the maintenance and stability of wind power system operation.

Suggested Citation

  • Yagang Zhang & Jingyun Yang & Kangcheng Wang & Yinding Wang, 2014. "Lorenz Wind Disturbance Model Based on Grey Generated Components," Energies, MDPI, vol. 7(11), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:11:p:7178-7193:d:42104
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    References listed on IDEAS

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    Cited by:

    1. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    2. Yagang Zhang & Jingyun Yang & Kangcheng Wang & Zengping Wang, 2015. "Wind Power Prediction Considering Nonlinear Atmospheric Disturbances," Energies, MDPI, vol. 8(1), pages 1-15, January.

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