含嵌入式直流的受端电网动态响应智能分析方法

直流输电系统嵌入大型交流系统，将进一步增加换相失败、直流闭锁等故障对系统安全稳定的影响，交流系统与直流系统的交互作用，将使系统动态更加复杂；针对电网不同运行方式下大扰动后系统复杂动态响应特性分析难题，本文提出一种基于机电-电磁混合仿真与机器学习的智能分析方法。该方法基于PCA降维、DBSCAN、Kmeans等算法建立二阶段聚类模型，可针对直流落点近区严重故障后大量混合仿真动态曲线在高维空间中自动聚类，并给出相应标识与严重程度，提取交直流系统在不同故障下的典型动态模式，并自动标注并识别各模式下主导安全稳定问题。本文所提方法的有效性在2025年华东电网运行方式中得以验证，仿真结果标明，所提方法可有效提取不同故障下系统动态模式，将有效支撑后续复杂故障下的交直流系统动态机理分析。

Intelligent analysis method for dynamic response of receiving system with embedded HVDC

HVDC system embedded in large-scale AC power systems will further increase the impact of commutation failure, DC blocking and other faults on system security and stability. The interaction between AC system and DC system will make the system dynamics more complex. In this paper, an intelligent analysis method based on electromechanic-electromagnetic hybrid simulation and machine learning is proposed to analyze the complex dynamic response characteristics of power system after large disturbances under different operation modes. This method builds a two-stage clustering model based on PCA dimensionality reduction, DBSCAN, Kmeans and other algo-rithms, which can automatically cluster a large number of hybrid simulation dynamic curves in high-dimensional space after serious faults near the DC connection point, give corresponding identification and severity, and extract typical dynamic patterns of AC-DC sys-tems under different faults. It automatically label and identify the leading security and stability problems in each mode. The effectiveness of the proposed method has been verified in the planning model of East China Power Grid in 2025, and the simulation results show that the proposed method can effectively extract the system dynamic mode under different faults, and will effectively support the dynamic mechanism analysis of AC/DC system under subsequent complex faults.