Vulnerability assessment and optimization of distribution network data flow based on K-means clustering analysis
Chao Yang, Tong Li, Yulin Zhou, Gang Chen, Feng Jie Sun, Shujun Yang, Yang Liu, Shuai Ren, Jian Chen, Defeng Chen, Zhibin Yang, Hongbi Geng, Ruitong Liu · Journal of Physics Conference Series · 2025
Abstract With the rapid development of smart grids and distribution networks, the modern distribution network is faced with increasingly complex operating environments and higher security requirements. Distribution networks need to cope not only with fluctuations in traditional loads, but also with multiple factors such as distributed energy access, climate change and cyber attacks. Especially in the context of dynamic data flow, the operating state of the distribution network will show strong time-variability and complexity, which makes the assessment and optimization of the vulnerability of the distribution network particularly important. The K-means clustering analysis method can effectively identify potentially vulnerable areas and key nodes in the system by clustering the real-time situation of the dynamic data flow of the distribution network. This paper aims to make an in-depth analysis of the data flow of the distribution network by using the K-means clustering analysis method combined with weight factors, and evaluate the vulnerability of the distribution network under different working periods.