基于极大似然辨识的电网节点惯量估计方法
作者:
作者单位:

(1.国网湖南省电力有限公司,湖南 长沙 410007;2.湖南大学电气与信息工程学院,湖南 长沙 410082)

通讯作者:

孙铭锐(2002—),男,硕士研究生,主要从事低惯量电力系统运行与分析等方面的研究;E‐mail:2375340001@qq.com

中图分类号:

TM73

基金项目:

国家自然科学基金资助项目(52077066);国网湖南省电力有限公司科技项目(5216A5220023)


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

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

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    摘要:

    近年来,随着风电、光伏等电力电子接口电源大规模接入电网,电网整体惯量水平持续降低,节点惯量呈现出空间分布差异,系统频率失稳风险显著增加。因此,亟待快速评估电网惯量分布情况,以便调度运行人员及时制定有效惯量调控措施。提出了一种基于极大似然辨识的电网节点惯量估计方法。先利用频率和有功功率量测数据,构建用于惯量估计的带外部输入的自回归滑动平均模型(autoregressive moving average model with exogenous inputs,ARMAX);再利用极大似然辨识方法,识别ARMAX模型中的未知参数;然后,结合节点有功?频率传递函数和计数器确定惯量估计值和所需最小量测数据长度;最后,基于改进的CEPRI?36点系统进行的仿真测试验证了该方法的有效性。

    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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引用本文

姜新凡,刘永刚,孙铭锐,等.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,2025,40(2):21-29.
JIANG Xinfan, LIU Yonggang, SUN Mingrui, et al. 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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  • 在线发布日期: 2025-06-06
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