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Optimal design of probabilistic robust damping controllers to suppress multiband oscillations of power systems integrated with wind farm

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  • Yan, Cai
  • Yao, Wei
  • Wen, Jianfeng
  • Fang, Jiakun
  • Ai, Xiaomeng
  • Wen, Jinyu

Abstract

This paper proposes a general optimal design method of probabilistic robust damping controllers (PRDCs) to suppress multiband oscillations in the power system integrated with wind farm. The proposed optimal design method consists of the following two steps. In the first step, owing to the high efficiency of the probabilistic collocation method (PCM), it is adopted to investigate the probabilistic small signal stability analysis (PSSSA) of power system integrated with wind farm. In the second step, a novel adaptive compass search (ACS) with an adaptive sequence of exploration directions is proposed to enhance the global searching ability through the previous searching results, and the proposed ACS is used to obtain the parameters of the PRDCs via solving an optimization problem based on the results of PSSSA obtained from the first step. Case studies are conducted on 16-machine 68-bus system to verify the accuracy and computational efficiency of the PCM. Moreover, simulation studies are also conducted to verify the advantages of the control performances of ACS in the design of damping controllers compared with that of the traditional residue method, particle swarm optimization (PSO), grey wolf optimizer (GWO), and teaching learning-based optimization (TLBO), respectively. Finally, the effectiveness of the proposed method is validated by the time-domain simulation.

Suggested Citation

  • Yan, Cai & Yao, Wei & Wen, Jianfeng & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu, 2020. "Optimal design of probabilistic robust damping controllers to suppress multiband oscillations of power systems integrated with wind farm," Renewable Energy, Elsevier, vol. 158(C), pages 75-90.
    • Handle: RePEc:eee:renene:v:158:y:2020:i:c:p:75-90
    DOI: 10.1016/j.renene.2020.05.008
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    References listed on IDEAS

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    1. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    2. Chen, Jian & Yao, Wei & Zhang, Chuan-Ke & Ren, Yaxing & Jiang, Lin, 2019. "Design of robust MPPT controller for grid-connected PMSG-Based wind turbine via perturbation observation based nonlinear adaptive control," Renewable Energy, Elsevier, vol. 134(C), pages 478-495.
    3. Wang, Qin & Yao, Wei & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu & Yang, Xiaobo & Xie, Hailian & Huang, Xing, 2020. "Dynamic modeling and small signal stability analysis of distributed photovoltaic grid-connected system with large scale of panel level DC optimizers," Applied Energy, Elsevier, vol. 259(C).
    4. Yang, Bo & Yu, Tao & Shu, Hongchun & Zhang, Yuming & Chen, Jian & Sang, Yiyan & Jiang, Lin, 2018. "Passivity-based sliding-mode control design for optimal power extraction of a PMSG based variable speed wind turbine," Renewable Energy, Elsevier, vol. 119(C), pages 577-589.
    5. 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:

    1. Zhang, Jingjing & Li, Huanhuan & Chen, Diyi & Xu, Beibei & Mahmud, Md Apel, 2021. "Flexibility assessment of a hybrid power system: Hydroelectric units in balancing the injection of wind power," Renewable Energy, Elsevier, vol. 171(C), pages 1313-1326.
    2. Zhang, Guozhou & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2021. "A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration," Renewable Energy, Elsevier, vol. 178(C), pages 363-376.

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