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Model of Power System Stabilizer Adapting to Multi-Operating Conditions of Local Power Grid and Parameter Tuning

Author

Listed:
  • Wenping Hu

    (State Grid Hebei Electric Power Co., Ltd., Technology Research Institute, Shijiazhuang 050021, China)

  • Jifeng Liang

    (State Grid Hebei Electric Power Co., Ltd., Technology Research Institute, Shijiazhuang 050021, China)

  • Yitao Jin

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Fuzhang Wu

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

Abstract

The rapid development of the modern power grid has resulted in significant changes to the dynamic characteristics of regional power grids. Moreover, the operating conditions of power grids are increasingly complex, and uncertainty factors are on the rise, which makes it difficult for a conventional power system stabilizer (PSS) to provide enough damping for the power system. To solve the problem where the conventional model and parameter-tuning method of a PSS cannot adapt to the multi-operating conditions of the modern power system, a new emergency control model of PSS (E-PSS) that can adapt to the multi-operating conditions of the local power grid and a method of parameter tuning based on probabilistic eigenvalue are proposed in this paper. An emergency control channel is also installed on the PSS2B. The conventional channel is used to control the system under normal operating conditions, which ensures that the system meets the conditions of dynamic stability in 99% of operating conditions, and the emergency control is adopted immediately in extreme conditions. Through the process of parameter tuning, the adaptability of the PSS to multi-operating conditions and damping coupling are both considered. Finally, it is verified that the emergency control model of the PSS and the parameter tuning method are effective and robust by a series of simulations based on MATLAB and its Power system analysis toolbox (PSAT). The rapidity of the emergency control can guarantee its effectiveness.

Suggested Citation

  • Wenping Hu & Jifeng Liang & Yitao Jin & Fuzhang Wu, 2018. "Model of Power System Stabilizer Adapting to Multi-Operating Conditions of Local Power Grid and Parameter Tuning," Sustainability, MDPI, vol. 10(6), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:2089-:d:153368
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    Citations

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    Cited by:

    1. Gang Chen & Chang Liu & Chengwei Fan & Xiaoyan Han & Huabo Shi & Guanhong Wang & Dongping Ai, 2020. "Research on Damping Control Index of Ultra-Low-Frequency Oscillation in Hydro-Dominant Power Systems," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    2. Aliyu Sabo & Noor Izzri Abdul Wahab & Mohammad Lutfi Othman & Mai Zurwatul Ahlam Mohd Jaffar & Hakan Acikgoz & Hamzeh Beiranvand, 2020. "Application of Neuro-Fuzzy Controller to Replace SMIB and Interconnected Multi-Machine Power System Stabilizers," Sustainability, MDPI, vol. 12(22), pages 1-42, November.
    3. Farag Ali El-Sheikhi & Hisham M. Soliman & Razzaqul Ahshan & Eklas Hossain, 2021. "Regional Pole Placers of Power Systems under Random Failures/Repair Markov Jumps," Energies, MDPI, vol. 14(7), pages 1-14, April.
    4. Zhang, Jingjing & Mahmud, Apel & Govaerts, Willy & Chen, Diyi & Xu, Beibei & Xiong, Hualin, 2020. "Sensitivity analysis and low frequency oscillations for bifurcation scenarios in a hydraulic generating system," Renewable Energy, Elsevier, vol. 162(C), pages 334-344.
    5. Tawfik Guesmi & Badr M. Alshammari & Yasser Almalaq & Ayoob Alateeq & Khalid Alqunun, 2021. "New Coordinated Tuning of SVC and PSSs in Multimachine Power System Using Coyote Optimization Algorithm," Sustainability, MDPI, vol. 13(6), pages 1-18, March.

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