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Prediction of wind power ramp events based on residual correction

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  • Ouyang, Tinghui
  • Zha, Xiaoming
  • Qin, Liang
  • He, Yusen
  • Tang, Zhenhao

Abstract

Wind power ramps cause large-amplitude power fluctuation which harmfully affects the stability of power system’s operation. As a new issue in wind power integration, the existing ramp forecasting methods still has some imperfection, e.g., harmonization on long-term trend and short-term precision. Therefore, an advanced method is proposed in this paper, mainly focus on improving the performance of wind power ramp prediction. This method utilizes wind power curve to build a primary model which can capture the trend of wind power variation. Then, prediction residual of the primary model is corrected by a MSAR (Markov-Switching-Auto-Regression) model which combining the advantages of AR models and Markov chain. Finally, an improved swinging door algorithm is applied to extract linear segments, and ramp definitions are used to detect ramp events. Actual wind farm data is used to test the proposed method. Comparison with traditional methods are presented, the numerical results validate that the proposed approach has improved performance not only on wind power prediction but also on ramp prediction.

Suggested Citation

  • Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & He, Yusen & Tang, Zhenhao, 2019. "Prediction of wind power ramp events based on residual correction," Renewable Energy, Elsevier, vol. 136(C), pages 781-792.
    • Handle: RePEc:eee:renene:v:136:y:2019:i:c:p:781-792
    DOI: 10.1016/j.renene.2019.01.049
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    Cited by:

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    2. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    3. Li Han & Yan Qiao & Mengjie Li & Liping Shi, 2020. "Wind Power Ramp Event Forecasting Based on Feature Extraction and Deep Learning," Energies, MDPI, vol. 13(23), pages 1-19, December.
    4. Meng, Anbo & Chen, Shu & Ou, Zuhong & Xiao, Jianhua & Zhang, Jianfeng & Chen, Shun & Zhang, Zheng & Liang, Ruduo & Zhang, Zhan & Xian, Zikang & Wang, Chenen & Yin, Hao & Yan, Baiping, 2022. "A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network," Energy, Elsevier, vol. 261(PA).
    5. Hu, Jianming & Zhang, Liping & Tang, Jingwei & Liu, Zhi, 2023. "A novel transformer ordinal regression network with label diversity for wind power ramp events forecasting," Energy, Elsevier, vol. 280(C).
    6. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2022. "Ramp Rate Limitation of Wind Power: An Overview," Energies, MDPI, vol. 15(16), pages 1-15, August.
    7. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    8. Yin, Linfei & Wu, Yunzhi, 2022. "Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy," Applied Energy, Elsevier, vol. 307(C).
    9. Sizu Hou & Yisu Hou & Baikui Li & Ziqi Wang, 2023. "Fault Recovery Strategy for Power–Communication Coupled Distribution Network Considering Uncertainty," Energies, MDPI, vol. 16(12), pages 1-21, June.
    10. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    11. He, Yaoyao & Zhu, Chuang & An, Xueli, 2023. "A trend-based method for the prediction of offshore wind power ramp," Renewable Energy, Elsevier, vol. 209(C), pages 248-261.

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