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A Singular Spectrum Analysis and Gaussian Process Regression-Based Prediction Method for Wind Power Frequency Regulation Potential

Author

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  • Xianbo Du

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Jilai Yu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

Abstract

The development of primary frequency regulation (FR) technology has prompted wind power to provide support for active power control systems, and it is critical to accurately assess and predict the wind power FR potential. Therefore, a prediction model for wind power virtual inertia and primary FR potential is proposed. Firstly, the primary FR control mode is divided and the mapping relationship of operating wind speed and FR potential is constructed. Secondly, a hybrid prediction method of singular spectrum analysis (SSA) and Gaussian process regression (GPR) is proposed for predicting the speed of wind. Finally, the wind speed sequence is adopted to calculate the FR potential with various regulation modes in future time. The results show the advantages of the proposed method in the prediction accuracy of wind power FR potential and the ability to characterize the uncertainty information of the prediction results. Accurate modeling and prediction of wind power FR potential can significantly promote wind turbines to implement fine control of primary FR and optimal allocation of FR capacity within wind farm and group. Based on the actual operation data, the deterministic prediction and probability prediction of the FR potential of wind farms are conducted in this paper.

Suggested Citation

  • Xianbo Du & Jilai Yu, 2022. "A Singular Spectrum Analysis and Gaussian Process Regression-Based Prediction Method for Wind Power Frequency Regulation Potential," Energies, MDPI, vol. 15(14), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5126-:d:862830
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    References listed on IDEAS

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    1. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    2. Carlos E. Prieto Cerón & Luís F. Normandia Lourenço & Juan S. Solís-Chaves & Alfeu J. Sguarezi Filho, 2022. "A Generalized Predictive Controller for a Wind Turbine Providing Frequency Support for a Microgrid," Energies, MDPI, vol. 15(7), pages 1-20, April.
    3. Danny Ochoa & Sergio Martinez, 2021. "Analytical Approach to Understanding the Effects of Implementing Fast-Frequency Response by Wind Turbines on the Short-Term Operation of Power Systems," Energies, MDPI, vol. 14(12), pages 1-22, June.
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    1. Qu, Zhijian & Li, Jian & Hou, Xinxing & Gui, Jianglin, 2023. "A D-stacking dual-fusion, spatio-temporal graph deep neural network based on a multi-integrated overlay for short-term wind-farm cluster power multi-step prediction," Energy, Elsevier, vol. 281(C).

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