Dynamic Identification of Critical Nodes and Regions in Power Grid Based on Spatio-Temporal Attribute Fusion of Voltage Trajectory

Xiwei Bai, Daowei Liu, Jie Tan, Hongying Yang and Hengfeng Zheng
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Xiwei Bai: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Daowei Liu: China Electric Power Research Institute, Beijing 100192, China
Jie Tan: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Hongying Yang: China Electric Power Research Institute, Beijing 100192, China
Hengfeng Zheng: China Electric Power Research Institute, Beijing 100192, China

Energies, 2019, vol. 12, issue 5, 1-16

Abstract: Accurate identification of critical nodes and regions in a power grid is a precondition and guarantee for safety assessment and situational awareness. Existing methods have achieved effective static identification based on the inherent topological and electrical characteristics of the grid. However, they ignore the variations of these critical nodes and regions over time and are not appropriate for online monitoring. To solve this problem, a novel data-driven dynamic identification scheme is proposed in this paper. Three temporal and three spatial attributes are extracted from their corresponding voltage phasor sequences and integrated via Gini-coefficient and Spearman correlation coefficient to form node importance and relevance assessment indices. Critical nodes and regions can be identified dynamically through importance ranking and clustering on the basis of these two indices. The validity and applicability of the proposed method pass the test on various situations of the IEEE-39 benchmark system, showing that this method can identify the critical nodes and regions, locate the potential disturbance source accurately, and depict the variation of node/region criticality dynamically.

Keywords: critical node; critical region; spatio-temporal attribute fusion; node voltage trajectory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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