Data-Driven Identification of Critical Nodes and Prediction of Disturbance Propagation

WU Qian; ZHANG Dongxia; LONG Wangcheng; WANG Ning; RAO Jianye

doi:10.12204/j.issn.1000-7229.2026.04.004

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Electric Power Construction ›› 2026, Vol. 47 ›› Issue (4) : 39-48. DOI: 10.12204/j.issn.1000-7229.2026.04.004

Risk Assessment and Risk Control for New Power System·Hosted by CHEN Haoyong, ZHANG Yongjun, ZHANG Pei, YE Yujian, XIAO Dongliang·

Data-Driven Identification of Critical Nodes and Prediction of Disturbance Propagation

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Abstract

[Objective] Power system data demonstrates features of high stochasticity， strong interaction， and multi-dimensional coupling. Traditional mechanistic modeling and simulation analyses， which rely on conditional assumptions and model simplifications， fail to meet the real-time defense requirements of large-scale power grids in terms of timeliness. By fully leveraging wide-area spatiotemporal sequence information from power grids， we can enhance analysis accuracy and timeliness while transcending the limitations of mechanistic models. This approach enables multi-level situational awareness of the physical operational states of power systems， facilitating more effective real-time defense strategies. [Methods] This paper presents a method to promote the safe and stable operation of the power grid. Based on the dynamic characteristics of the power grid， the spatiotemporal data are analyzed and excavated by random matrix theory. This method overcomes the limitations of the traditional grid-based physical topology， realizing the real-time assessment and identification of critical nodes in the system. According to assessment results of critical nodes， the disturbance propagation is predicted based on data-driven Markov chain. The method achieves the personalized risk assessment of grid nodes and collective predictive analysis of grid situations. [Results] This paper focuses on personalized risk assessment of nodes in the power grid， identifying risk nodes or key nodes in the system. By using the method proposed in this paper to explore the transition from monitoring and warning to predictive warning. The effectiveness and rationality of the proposed method can be verified through case analysis by using Markov chain to predict the propagation of disturbances relative to disturbed nodes. This study enables early warning of the disturbance propagation when the power grid disturbance occurs， and focuses on the critical nodes that will cause serious accidents in advance. Compared with other identification methods， the proposed approach takes into account both the electrical characteristics and topological properties of the system， with its identification results dynamically adapting to changes in system operating conditions. [Conclusions] Prevention and control are conducted in advance to ensure the safe and stable operation of the system， which is of great significance to the defense of blackouts.

Key words

data mining / stochastic matrix / critical nodes / disturbance propagation

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WU Qian , ZHANG Dongxia , LONG Wangcheng , et al . Data-Driven Identification of Critical Nodes and Prediction of Disturbance Propagation[J]. Electric Power Construction. 2026, 47(4): 39-48 https://doi.org/10.12204/j.issn.1000-7229.2026.04.004

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