Method for power grid nodal inertia estimation based on maximum likelihood identification
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Affiliation:

(1. State Grid Hunan Electric Power Co., Ltd., Changsha 410007, China; 2. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

Clc Number:

TM73

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    Abstract:

    In recent years, with the large-scale integration of power electronic interfaced power sources such as wind power and photovoltaics into the power grid, the overall inertia level of the power grid has decreased, and the nodal inertia has shown spatial distribution differences, significantly increasing the risk of system frequency instability. It is urgent to quickly evaluate the distribution of inertia in the power grid so that dispatch and operation personnel can timely formulate effective inertia control measures. Therefore, a method for estimating the power grid nodal inertia based on maximum likelihood identification is proposed. Firstly, by using frequency and active power measurement data, the autoregressive moving average model with exogenous inputs (ARMAX) for inertia estimation is constructed. Secondly, the unknown parameters in the ARMAX model are identified via the maximum likelihood identification method. Additionally, the transfer function of active power and frequency of the node and counter are employed to determine the estimated inertia and the required minimal measurement data length. Finally, the simulation test is conducted based on the improved CEPRI-36 node system to verify the effectiveness of the proposed method.

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姜新凡,刘永刚,孙铭锐,吴晋波,汪婧文,文云峰.基于极大似然辨识的电网节点惯量估计方法[J].电力科学与技术学报英文版,2025,40(2):21-29. JIANG Xinfan, LIU Yonggang, SUN Mingrui, WU Jinbo, WANG Jingwen, WEN Yunfeng. Method for power grid nodal inertia estimation based on maximum likelihood identification[J]. Journal of Electric Power Science and Technology,2025,40(2):21-29.

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  • Online: June 06,2025
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