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Robustness and adaptability analysis for equivalent model of doubly fed induction generator wind farm using measured data

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  • Zhang, Jian
  • Cui, Mingjian
  • He, Yigang

Abstract

As many large wind farms connected to the power grid, it is necessary to develop a robust and adaptable dynamic equivalent model of the wind farm for system analysis and control. In this paper, the trajectory sensitivity of time-varying parameters of the equivalent model is analyzed. Then the non-time- varying parameters of the equivalent model are fixed as aggregated values, while the time-varying parameters are identified using the genetic learning particle swarm optimization based on phasor measurement unit data at the point of interconnection. The robustness and adaptability of the equivalent model under different scenarios are analyzed. The simulation results using the Western Electricity Coordinating Council benchmark test system show that the global searching capability to find the optimal point of the proposed method is higher than canonical particle swarm optimization and genetic algorithm by 2 orders. Further, the biggest mismatch between the identification results of the proposed method and the true values is within 10% for parameters with high sensitivity which is much better than previous work.

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  • Zhang, Jian & Cui, Mingjian & He, Yigang, 2020. "Robustness and adaptability analysis for equivalent model of doubly fed induction generator wind farm using measured data," Applied Energy, Elsevier, vol. 261(C).
    • Handle: RePEc:eee:appene:v:261:y:2020:i:c:s0306261919320495
    DOI: 10.1016/j.apenergy.2019.114362
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    References listed on IDEAS

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    1. Fernández, Luis M. & Jurado, Francisco & Saenz, José Ramón, 2008. "Aggregated dynamic model for wind farms with doubly fed induction generator wind turbines," Renewable Energy, Elsevier, vol. 33(1), pages 129-140.
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    4. Yang, Bo & Yu, Tao & Shu, Hongchun & Dong, Jun & Jiang, Lin, 2018. "Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers," Applied Energy, Elsevier, vol. 210(C), pages 711-723.
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    Cited by:

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    2. Shicong Zhang & Zilong Yu & Bowen Zhou & Zhile Yang & Dongsheng Yang, 2020. "A Decentralized Optimization Strategy for Distributed Generators Power Allocation in Microgrids Based on Load Demand–Power Generation Equivalent Forecasting," Energies, MDPI, vol. 13(3), pages 1-20, February.
    3. Wang, Huaizhi & Liu, Yangyang & Zhou, Bin & Voropai, Nikolai & Cao, Guangzhong & Jia, Youwei & Barakhtenko, Evgeny, 2020. "Advanced adaptive frequency support scheme for DFIG under cyber uncertainty," Renewable Energy, Elsevier, vol. 161(C), pages 98-109.
    4. Aya M. Moheb & Enas A. El-Hay & Attia A. El-Fergany, 2022. "Comprehensive Review on Fault Ride-Through Requirements of Renewable Hybrid Microgrids," Energies, MDPI, vol. 15(18), pages 1-30, September.
    5. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Zhao, Xin & Liu, Yu & Guo, Yasen & Wang, Sicheng, 2020. "A novel robust security constrained unit commitment model considering HVDC regulation," Applied Energy, Elsevier, vol. 278(C).
    6. Jian Zhang & Mingjian Cui & Yigang He, 2020. "Parameters Identification of Equivalent Model of Permanent Magnet Synchronous Generator (PMSG) Wind Farm Based on Analysis of Trajectory Sensitivity," Energies, MDPI, vol. 13(18), pages 1-18, September.
    7. Zhou, Yu & Li, Zhengshuo & Wang, Guangrui, 2021. "Study on leveraging wind farms' robust reactive power range for uncertain power system reactive power optimization," Applied Energy, Elsevier, vol. 298(C).
    8. Zong, Haoxiang & Lyu, Jing & Wang, Xiao & Zhang, Chen & Zhang, Ruifang & Cai, Xu, 2021. "Grey box aggregation modeling of wind farm for wideband oscillations analysis," Applied Energy, Elsevier, vol. 283(C).

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