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

Citation: YE Lin,LU Peng,TENG Jingzhu,ZHAI Bingxu,WU Linlin,LAN Haibo,ZHONG Wuzhi.Power Prediction and Correction Model Considering Wind Power Ramping Events[J].Automation of Electric Power Systems. DOI: 10.7500/AEPS20180321004 copy

Power Prediction and Correction Model Considering Wind Power Ramping Events

  • Received Date: March 21, 2018
    Accepted Date: October 10, 2018
    Available Online: 2019-01-31

  • Abstract:

        With the continuously increasing integration capacity of wind power into power system, ramping events of large scale highly-concentrated wind power generation have brought series of challenges on power system operation. A novel method of multi-time scale power prediction and correction model considering the characteristic of wind power ramping events is employed based on extreme learning machine (ELM). Firstly, the optimized swinging door algorithm (OpSDA) is utilized to identify the wind power ramp events (WPRE). Then, the fuzzy C-means clustering model is established to describe the characteristic of the WPRE as well as to get the congeneric data. ELM is used as a predictor to forecast the same type of data separately. Wind power forecasting result shows a good agreement with historic event database by use of the tuple vector time warp (TVTW) algorithm. Finally, the wind power prediction result is corrected corresponding to the difference between the predicted power history value and the actual value. Case studies show that the proposed approach significantly improves the precision of wind power forecasting in wind power ramping periods.

  • Keywords:

    Wind power ramp event; Optimized swinging door algorithm; FS-ELM neural network; wind power forecast

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