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Refined Equivalent Modeling Method for Mixed Wind Farms Based on Small Sample Data

Author

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  • Qianlong Zhu

    (State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China
    The School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China)

  • Wenjing Xiong

    (The School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China)

  • Haijiao Wang

    (State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China)

  • Xiaoqiang Jin

    (The School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China)

Abstract

For equivalent modeling of mixed wind farms (WFs), existing clustering indicators cannot consider the complex coupling characteristics between different types of wind turbines (WTs). In this paper, a refined equivalent modeling approach based on artificial intelligence technology is proposed. Firstly, the electromechanical transient performance of mixed WFs is analyzed. The WT type, wind speed and direction, and voltage dip are considered the dominant factors affecting the external dynamic response of mixed WFs. Secondly, the equivalent node model is established, including the selection of independent and dependent variables. Then, the multiple artificial neural networks (ANNs) are trained one by one based on small sample data, to fit the nonlinear relationship between the dependent variables and the independent variables. Finally, the dynamic response of the power systems with a mixed WF is simulated in the MATLAB platform. A comparison of the errors in electromechanical phenomena demonstrates that the proposed model can reflect the external characteristics of the test mixed WF in different wind conditions and voltage dips.

Suggested Citation

  • Qianlong Zhu & Wenjing Xiong & Haijiao Wang & Xiaoqiang Jin, 2023. "Refined Equivalent Modeling Method for Mixed Wind Farms Based on Small Sample Data," Energies, MDPI, vol. 16(20), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7191-:d:1264680
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    References listed on IDEAS

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    1. Songjune Lee & Seungjin Kang & Gwang-Se Lee, 2023. "Predictions for Bending Strain at the Tower Bottom of Offshore Wind Turbine Based on the LSTM Model," Energies, MDPI, vol. 16(13), pages 1-18, June.
    2. Jiawei Wu & Jinyu Xiao & Jinming Hou & Xunyan Lyu, 2023. "Development Potential Assessment for Wind and Photovoltaic Power Energy Resources in the Main Desert–Gobi–Wilderness Areas of China," Energies, MDPI, vol. 16(12), pages 1-22, June.
    3. Qianlong Zhu & Ming Ding & Pingping Han, 2016. "Equivalent Modeling of DFIG-Based Wind Power Plant Considering Crowbar Protection," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-16, August.
    4. Abhinandan Routray & Yiza Srikanth Reddy & Sung-ho Hur, 2023. "Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
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