| 引用本文: | 周书宇,杨晶莹,蔡国伟,等.基于S-Koopman能量矩阵的互联电网扰动事件分类及区域定位[J].电力系统保护与控制,2025,53(11):105-115. |
| ZHOU Shuyu,YANG Jingying,CAI Guowei,et al.Disturbance event classification and regional localization in interconnected power grids based on the S-Koopman energy matrix[J].Power System Protection and Control,2025,53(11):105-115 |
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| 摘要: |
| 扰动类型及扰动发生的区域位置信息对互联电网稳定分析与调控具有重要作用。为此，提出了一种基于S-Koopman能量矩阵的扰动事件分类及区域定位方法。该方法利用子空间最优模式分解算法(subspace optimal mode decomposition algorithm, Sub-OpMD)计算得到包含随机系统动态信息的S-Koopman算子的低维最优矩阵。并根据最优低维矩阵的特征分解结果构建S-Koopman能量矩阵。S-Koopman能量矩阵与系统动态行为相关；其行/列向量可以提取系统扰动事件发生后的不同特征数据以及不同位置PMU数据波动强弱。并进一步根据不同扰动类型以及扰动区域下的PMU数据动态变化规律，提出系统扰动事件分类及区域定位的量化评估指标。新英格兰10机39节点仿真系统和CEPRI-SSFS 197节点实际系统的计算和分析验证了所提方法的合理性、有效性和准确性。 |
| 关键词: 扰动事件分类 扰动区域定位 S-Koopman能量矩阵 量化指标 |
| DOI：10.19783/j.cnki.pspc.241013 |
| 分类号: |
| 基金项目:国家重点研发计划项目资助(2021YFB2400800)“响应驱动的大电网稳定性智能增强分析与控制技术” |
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| Disturbance event classification and regional localization in interconnected power grids based on the S-Koopman energy matrix |
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ZHOU Shuyu1, 2, YANG Jingying1, 2, CAI Guowei1, LIU Cheng1, JIANG Chao1
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1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education
(Northeast Electric Power University), Jilin 132012, China; 2. State Grid Jilin Electric Power Co., Ltd., Changchun 132000, China
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| Abstract: |
| The classification of disturbance event types and the identification of their regional locations are critical for the stability analysis and control of interconnected power grids. To address this, a disturbance event classification and localization method based on the S-Koopman energy matrix is proposed. This method uses the subspace optimal mode decomposition algorithm (Sub-OpMD) to calculate the optimal low dimensional matrix of the S-Koopman operator, which captures the dynamic information of the stochastic system. The S-Koopman energy matrix is then constructed according to the eigen decomposition results of the optimal low dimensional matrix. The S-Koopman energy matrix reflects the dynamic behavior of the system; its row/column vectors can extract different feature data following disturbance events, as well as the fluctuation strengths of PMU data at different positions. Furthermore, by analyzing the dynamic variation patterns of PMU data under different disturbance types and regions, quantitative indicators of disturbance event classification and regional localization are proposed. The calculation and analysis of the New England 10-generator 39-bus simulation system and the CEPRI-SSFS 197-bus real-world system verify the rationality, effectiveness, and accuracy of the proposed method. |
| Key words: disturbance event classification disturbance regional localization S-Koopman energy matrix quantitative indicator |
