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A Snake Optimization Algorithm-Based Power System Inertia Estimation Method Considering the Effects of Transient Frequency and Voltage Changes

Author

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  • Yanzhen Pang

    (School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Feng Li

    (School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Haiya Qian

    (School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Xiaofeng Liu

    (School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Yunting Yao

    (School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China)

Abstract

Inertia is the measure of a power system’s ability to resist power interference. The accurate estimation and prediction of inertia are crucial for the safe operation of the power system. To obtain the accurate power system inertia provided by generators, this paper proposes an estimation method considering the influence of frequency and voltage characteristics on the power deficit during transients. Specifically, the traditional swing equations-based inertia estimation model is improved by embedding linearized frequency and voltage factors. On this basis, the snake optimization algorithm is utilized to identify the power system inertia constant due to its strong global search ability and fast convergence speed. Finally, the proposed inertia estimation method is validated in four test systems, and the results show the effectiveness of the proposed method.

Suggested Citation

  • Yanzhen Pang & Feng Li & Haiya Qian & Xiaofeng Liu & Yunting Yao, 2024. "A Snake Optimization Algorithm-Based Power System Inertia Estimation Method Considering the Effects of Transient Frequency and Voltage Changes," Energies, MDPI, vol. 17(17), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4430-:d:1471016
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    References listed on IDEAS

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    1. Davide del Giudice & Samuele Grillo, 2019. "Analysis of the Sensitivity of Extended Kalman Filter-Based Inertia Estimation Method to the Assumed Time of Disturbance," Energies, MDPI, vol. 12(3), pages 1-19, February.
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