Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

نویسندگان

  • Yagang Zhang
  • Jingyun Yang
  • Kangcheng Wang
  • Zengping Wang
  • Frede Blaabjerg
چکیده

This paper considers the effect of nonlinear atmospheric disturbances on wind power prediction. A Lorenz system is introduced as an atmospheric disturbance model. Three new improved wind forecasting models combined with a Lorenz comprehensive disturbance are put forward in this study. Firstly, we define the form of the Lorenz disturbance variable and the wind speed perturbation formula. Then, different artificial neural network models are used to verify the new idea and obtain better wind speed predictions. Finally we separately use the original and improved wind speed series to predict the related wind power. This proves that the corrected wind speed provides higher precision wind power predictions. This research presents a totally new direction in the wind prediction field and has profound theoretical research value and practical guiding significance.

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تاریخ انتشار 2015