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Wind Power Ramp Event Forecasting Based on Feature Extraction and Deep Learning

Author

Listed:
  • Li Han

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Yan Qiao

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Mengjie Li

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Liping Shi

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

In order to improve the accuracy of wind power ramp forecasting and reduce the threat of ramps to the safe operation of power systems, a wind power ramp event forecast model based on feature extraction and deep learning is proposed in this work. Firstly, the Optimized Swinging Door Algorithm (OpSDA) is introduced to detect wind power ramp events, and the extraction results of ramp features, such as the ramp rate, are obtained. Then, a ramp forecast model based on a deep learning network is established. The historical wind power and its ramp features are used as the input of the forecast model, thereby strengthening the model’s learning for ramp features and preventing ramp features from being submerged in the complex wind power signal. A Convolutional Neural Network (CNN) is adopted to extract features from model inputs to obtain the coupling relationship between wind power and ramp features, and Long Short-Term Memory (LSTM) is utilized to learn the time-series relationship of the data. The forecast wind power is used as the output of the model, based on which the ramp forecast result is obtained after the ramp detection. Finally, the wind power data from the Elia website is used to verify the forecast performance of the proposed method for wind power ramp events.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6449-:d:457581
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    References listed on IDEAS

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    1. 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.
    2. Yin, Hao & Ou, Zuhong & Huang, Shengquan & Meng, Anbo, 2019. "A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition," Energy, Elsevier, vol. 189(C).
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    Citations

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    Cited by:

    1. Junwei Fu & Yuna Ni & Yuming Ma & Jian Zhao & Qiuyi Yang & Shiyi Xu & Xiang Zhang & Yuhua Liu, 2023. "A Visualization-Based Ramp Event Detection Model for Wind Power Generation," Energies, MDPI, vol. 16(3), pages 1-16, January.
    2. António Couto & Paula Costa & Teresa Simões, 2021. "Identification of Extreme Wind Events Using a Weather Type Classification," Energies, MDPI, vol. 14(13), pages 1-16, July.
    3. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    4. Yun, Eunjeong & Hur, Jin, 2021. "Probabilistic estimation model of power curve to enhance power output forecasting of wind generating resources," Energy, Elsevier, vol. 223(C).
    5. Wei, Danxiang & Wang, Jianzhou & Niu, Xinsong & Li, Zhiwu, 2021. "Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks," Applied Energy, Elsevier, vol. 292(C).
    6. Brian Loza & Luis I. Minchala & Danny Ochoa-Correa & Sergio Martinez, 2024. "Grid-Friendly Integration of Wind Energy: A Review of Power Forecasting and Frequency Control Techniques," Sustainability, MDPI, vol. 16(21), pages 1-22, November.
    7. Cui, Yang & He, Yingjie & Xiong, Xiong & Chen, Zhenghong & Li, Fen & Xu, Taotao & Zhang, Fanghong, 2021. "Algorithm for identifying wind power ramp events via novel improved dynamic swinging door," Renewable Energy, Elsevier, vol. 171(C), pages 542-556.

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