| 引用本文: | 赵 伟,武家辉,买力哈巴,等.基于改进MLE参数辨识ARMAX模型的电力系统节点惯量评估[J].电力系统保护与控制,2025,53(16):39-49.[点击复制] |
| ZHAO Wei,WU Jiahui,MAI Lihaba,et al.Power system node inertia evaluation based on improved MLE parameter identification ARMAX model[J].Power System Protection and Control,2025,53(16):39-49[点击复制] |
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| 摘要: |
| 随着风电渗透率的持续上升，电力系统的惯量水平显著下降，对系统频率稳定性构成了新的挑战。为有效评估风电并网情况下电力系统节点惯量的变化，提出了一种基于受控自回归滑动平均(autoregressive moving average with exogenous variable, ARMAX)模型的改进最大似然估计(maximum likelihood estimation, MLE)参数辨识方法对系统机组直接相连节点进行惯量评估。首先，构建ARMAX模型对发电机组直接相连节点的动态特性进行建模，并利用改进MLE对模型参数进行辨识，以评估与机组直接相连的节点惯量。然后，基于k-means聚类算法对发电机组节点惯量进行分区，计算得到系统区域惯量和中心频率，并进一步对非发电机组节点频率进行自适应多项式拟合计算，得到其系统节点惯量。最后，搭建IEEE39含风力发电机组节点系统，绘制热力图直观展示电力系统节点和区域的惯量分布，验证了所提改进方法的有效性。该方法有助于精准识别系统中不同节点的动态响应特性，为风电并网系统的分析和规划提供了有力支持。 |
| 关键词: 最大似然 参数辨识 节点惯量 惯量分区 多项式拟合 |
| DOI：10.19783/j.cnki.pspc.241427 |
| 投稿时间：2024-10-25修订日期：2025-02-17 |
| 基金项目:国家自然科学基金项目资助(52167016)；新疆维吾尔自治区重点实验室开放课题(2023D04071)；新疆维吾尔自治区重点研发专项资助项目(2022B01020-3) |
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| Power system node inertia evaluation based on improved MLE parameter identification ARMAX model |
| ZHAO Wei1,WU Jiahui1,MAI Lihaba2,LI Wei2 |
| (1. Engineering Research Center of Renewable Energy Power Generation and Grid-connected Control, Ministry of
Education, Xinjiang University, Urumqi 830047, China; 2. State Grid Xinjiang Integrated Energy
Service Company Limited, Urumqi 830011, China) |
| Abstract: |
| With the continuous increase of wind power penetration, the inertia level of power systems decreases significantly, posing new challenges to system frequency stability. To effectively evaluate the change of power system node inertia under wind power integration, this paper proposes an improved maximum likelihood estimation (MLE) based on autoregressive moving average with exogenous variable (ARMAX) to evaluate the inertia at nodes directly connected to generation units. First, an ARMAX model is constructed to represent the dynamic characteristics of the nodes directly connected to generation units. The improved MLE method is then applied to identify model parameters and estimate the corresponding node inertia. Then, based on the k-means clustering algorithm, the generator nodes are partitioned according to their inertia, allowing for the calculation of the inertia and center frequency of the system region. Furthermore, adaptive polynomial fitting is employed to estimate the node inertia of non-generator nodes based on their frequency behavior. Finally, the IEEE39-node system including wind turbine generator nodes is modeled, and the heatmap is drawn to visually display the inertia distribution of the power system nodes and regions, which verifies the effectiveness of the improved method in this paper. This approach enables accurate identification of dynamic responses at various nodes and provides strong support for the analysis and planning of wind-integrated power systems. |
| Key words: maximum likelihood estimation (MLE) parameter identification node inertia inertia partitioning polynomial fitting |
