ISSN 1000-1026
CN 32-1180/TP
  • ISSN 1000-1026
  • CN 32-1180/TP

Citation: DING Ming,ZHANG Chao,WANG Bo,BI Rui,MIAO Leying,CHE Jianfeng.Short-term Forecasting and Error Correction of Wind Power Based on Power Fluctuation Process[J].Automation of Electric Power Systems,2019,43(3):2-9. DOI: 10.7500/AEPS20180322011 copy

Short-term Forecasting and Error Correction of Wind Power Based on Power Fluctuation Process

  • Received Date: March 22, 2018
    Accepted Date: September 30, 2018
    Available Online: December 19, 2018

  • Abstract:

        Wind resources have the characteristics of strong fluctuation, randomness and discontinuity, which lead to the low accuracy of wind power forecasting. In order to reduce the impact of wind power fluctuations on the power grid and improve the ability of power systems to accept and absorb wind power, an improved wind power short-term prediction method and fluctuation based error correction method are proposed. Firstly, the wind power is divided into clusters according to different fluctuation processes. The characteristic curves of different fluctuations are extracted to correct the power values. Secondly, the back propagation neural network optimized by gravitational search algorithm (GSA-BP) is used as the basic prediction method to predict. Then the performance of forecasting errors under different fluctuations is analyzed, and the mapping relationship between forecasting errors and comprehensive meteorological indicators is established. A corresponding wind power error correction model is established for different fluctuation processes. A combination of linear model and GSA-BP nonlinear model is proposed to modify the prediction error. Finally, the power prediction value is added to the prediction error correction value as the final forecast result. The wind power prediction error correction method not only involves conventional factors such as wind speed and direction, but also takes into account the fluctuation of wind power.

  • Keywords:

    wind power prediction; wave characteristics; neural network; gravitational search; error correction

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