A multi-strategy framework to evaluate seismic resilience improvement of substations

Abstract

Electrical substations suffer significant functionality and economic damage during earthquakes. To improve the seismic reliability and safety of substation systems, this study proposes a seismic resilience evaluation methodology based on the characteristics of the substation's functionality. A probability-based substation functionality network model was built using Bayesian network algorithms, and a stepped functional recovery function was established to quantitatively assess the resilience of substations. Moreover, a network seismic resilience improvement framework was constructed based on the seismic reliability and function recovery of substations. The approach develops network learning algorithms to determine the importance of substation equipment. Based on the proposed multi-strategy seismic resilience optimization algorithm, the post-earthquake repair path and combination strategies for substations were optimized through iterative simulation. Taking a typical 220 kV substation as a case study for seismic resilience analysis, the key equipment was obtained, and post-earthquake resource constraints on system functionality were identified. Notably, multi-dimensional seismic resilience improvement strategies were obtained. The analysis results provide references for post-earthquake resource allocation and economic benefit evaluation in practical engineering.