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A Local Quadratic Wavelet Neural Network Method for Predicting Dispersed Wind Power Generation

  • Yiqing Lian
  • Changcheng ZhouEmail author
  • Zhiyong Yuan
  • Jinyong Lei
  • Si-yu Tao
Conference paper
  • 94 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 585)

Abstract

In this paper, a new local quadratic wavelet neural network (LQWNN) method is proposed for short-term wind power generation prediction in power system. Due to the nonlinear structure of its local quadratic model (LQM), this method can effectively simulate the nonlinear behavior of wind power generation. In the time domain and frequency domain, wavelet function sets the effective region of LQMs in LQWNN, thus improving the learning efficiency and structural transparency of the overall model. The proposed LQWNN method employs a simple and effective particle swarm optimization (PSO) algorithm and optimizes its parameters. Through two real cases studies, namely the wind power forecast of the Irish power grid and the Jiuquan wind power plant in Gansu province, the performance of the proposed wind power forecasting method is evaluated and compared with other methods. The results show that the LQWNN method proposed in this paper is very effective in predicting wind power generation and has a good application prospect.

Keywords

Neural networks Local quadratic wavelets Particle swarm optimization Power systems Wind power forecasting 

Notes

Acknowledgements

Work in this paper is supported by “Science and Technology Project of China Southern Power Grid (ZBKJXM20180015)”.

References

  1. 1.
    Methaprayoon K, Yingvivatanapong C, Lee WJ, Liao JR (2007) An integration of ANN wind power estimation into unit commitment considering the forecasting uncertainty. IEEE Trans Ind Appl 43(6):1441–1448CrossRefGoogle Scholar
  2. 2.
    Kusiak A, Zheng H, Song Z (2009) Models for monitoring wind farm power. IEEE Trans Renew Energy 34:583–590CrossRefGoogle Scholar
  3. 3.
    Kavasseri RG, Seetharaman K (2009) Day-ahead wind speed forecasting using f-ARIMA models. IEEE Trans Renew Energy 34(5):1388–1393CrossRefGoogle Scholar
  4. 4.
    Catalao J, Pousinho H, Mendes V (2011) Hybrid intelligent approach for short-term wind power forecasting in Portugal. IET Renew Power Gener 5(3):251–257Google Scholar
  5. 5.
    Amjady N, Keynia F, Zareipour H (2011) Wind power prediction by a new forecast engine composed of modified hybrid neural network and enhanced particle swarm optimization. IEEE Trans Sustain Energy 2(3):265–276CrossRefGoogle Scholar
  6. 6.
    Flores P, Tapia A, Tapia G (2005) Application of a control algorithm for wind speed prediction and active power generation. IEEE Trans Renew Energy 30:523–536CrossRefGoogle Scholar
  7. 7.
    Amjady N, Keynia F, Zareipour H (2005) Short-term wind power forecasting using ridgelet neural network. IEEE Trans Electr Power Syst Res 81:2099–2107CrossRefGoogle Scholar
  8. 8.
    Chen YH et al (2000) Evolving wavelet neural networks for system identification. Proc Int Conf Electr Eng 279–282Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yiqing Lian
    • 1
  • Changcheng Zhou
    • 1
    Email author
  • Zhiyong Yuan
    • 1
  • Jinyong Lei
    • 1
  • Si-yu Tao
    • 2
  1. 1.Electric Power Research Institute of China Southern Power GridGuangzhouChina
  2. 2.Southeast UniversityNanjingChina

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