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Analysis of the Sensitivity of Extended Kalman Filter-Based Inertia Estimation Method to the Assumed Time of Disturbance

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  • Davide del Giudice

    (Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, P.zza L. da Vinci 32, I-20133 Milano, Italy)

  • Samuele Grillo

    (Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, P.zza L. da Vinci 32, I-20133 Milano, Italy)

Abstract

The frequency behavior of an electric power system right after a power imbalance is determined by its inertia constant. The current shift in generation mix towards renewable energy sources is leading to a smaller and more variable inertia, thereby compromising the frequency stability of modern grids. Therefore, real-time inertia estimation methods would be beneficial for grid operators, as their situational awareness would be enhanced. This paper focuses on an inertia estimation method specifically tailored for synchronous generators, based on the extended Kalman filter (EKF). Such a method should be started at the time of disturbance, which must be estimated accurately, otherwise additional errors could be introduced in the inertia estimation process. In this paper, the sensitivity of the EKF-based inertia estimation method to the assumed time of disturbance is analyzed. It is shown that such sensitivity is influenced by the initially assumed inertia constant, the use time of the filter and by the time required for primary frequency regulation to be activated.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:483-:d:203200
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    References listed on IDEAS

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    1. Tielens, Pieter & Van Hertem, Dirk, 2016. "The relevance of inertia in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 999-1009.
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    Cited by:

    1. Daniele Linaro & Federico Bizzarri & Davide Giudice & Cosimo Pisani & Giorgio M. Giannuzzi & Samuele Grillo & Angelo M. Brambilla, 2023. "Continuous estimation of power system inertia using convolutional neural networks," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Feng Jiang & Fan Yang & Songjun Sun & Kai Yang, 2022. "Improved Linear Active Disturbance Rejection Control for IPMSM Drives Considering Load Inertia Mismatch," Energies, MDPI, vol. 15(3), pages 1-22, February.
    3. Stelios C. Dimoulias & Eleftherios O. Kontis & Grigoris K. Papagiannis, 2022. "Inertia Estimation of Synchronous Devices: Review of Available Techniques and Comparative Assessment of Conventional Measurement-Based Approaches," Energies, MDPI, vol. 15(20), pages 1-30, October.
    4. 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.
    5. Makolo, Peter & Zamora, Ramon & Lie, Tek-Tjing, 2021. "The role of inertia for grid flexibility under high penetration of variable renewables - A review of challenges and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).

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