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Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm

Author

Listed:
  • Fanjie Yang

    (School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Yun Zeng

    (School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Jing Qian

    (School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Youtao Li

    (School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Shihao Xie

    (School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China)

Abstract

Variations in generator parameters that occur during the operation of a doubly-fed induction wind turbine (DFIG) constitute a significant factor in the degradation of control performance. To address the problem of difficulty in identifying multiple parameters simultaneously in DFIG, a parameter identification method depending on the adaptive grey wolf algorithm with an information-sharing search strategy (ISIAGWO) is proposed to solve the problem of low accuracy and slow identification speed of multiple parameters in DFIG. The easily obtainable generator outlet current was selected as the observed quantity, and the trajectory sensitivity analysis was performed on the five electrical parameters of the DFIG to derive its discriminability. Finally, the parameter recognition of the DFIG was carried out using the ISIAGWO algorithm in the MATLAB/Simulink simulation software, applying the proposed calculation functions. The experimental results show that the ISIAGWO algorithm has better identification accuracy, stability, and convergence for DFIG’s generator parameter identification.

Suggested Citation

  • Fanjie Yang & Yun Zeng & Jing Qian & Youtao Li & Shihao Xie, 2023. "Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm," Energies, MDPI, vol. 16(3), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1355-:d:1048419
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    References listed on IDEAS

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    1. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
    2. Jia, Ke & Gu, Chenjie & Li, Lun & Xuan, Zhengwen & Bi, Tianshu & Thomas, David, 2018. "Sparse voltage amplitude measurement based fault location in large-scale photovoltaic power plants," Applied Energy, Elsevier, vol. 211(C), pages 568-581.
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