Graph-Based Spatiotemporal Data Modeling and Storage for Short-Circuit Current Analysis

Short-circuit current analysis in power systems is influenced not only by time but also by spatial locations. Temporal analysis of short-circuit currents requires investigating instantaneous variations, oscillations, harmonic components, etc., to understand the trends and waveforms. Analyzing the immediate impact of short-circuit currents on various system devices, including transformers, generators, and switchgear, is essential to assess equipment tolerance and transient response. Currently, the construction of power system graph models often utilizes relational databases or external storage methods, with opportunities for improvement in efficiency, consistency, and security. This paper fully leveraged the flexibility and scalability of graph databases, presenting a graph model construction approach based on temporal and spatial data storage in graph databases. This method comprehensively and accurately represented the spatiotemporal characteristics of power systems, aiding in short-circuit current analysis. The technique achieved storage and retrieval of historical data by associating spatiotemporal data with equipment nodes. Additionally, it correlated short-circuit current analysis results with power grid topology, enabling comprehensive analysis and prediction of power system operational states. This approach enriched short-circuit current analysis with abundant information, contributing to a more thorough understanding of power system behavior.