Optimization Strategy for Seismic Performance Enhancement of Substation Systems

Under the impact of earthquakes, substation systems may experience equipment failures, prolonged functional recovery periods, and expanded power outage consequences. This paper proposes a collaborative optimization framework for enhancing the seismic performance of substation systems and deploying repair resources, aimed at pre-earthquake planning. The system function is characterized by the outage availability of feeders weighted by load and user importance, and the user outage cost is explicitly defined as an economic consequence indicator with monetary units, rather than a dimensionless resilience index. A directed graph model is constructed to simulate the post-earthquake functional recovery process, and under given seismic hazard and vulnerability parameters, Monte Carlo sampling is used to capture the randomness of equipment condition failures and repair durations. Sensitivity analysis is employed to identify critical equipment, and the elitism-preservation and adaptive evolution non-dominated sorting genetic algorithm II (ERA-NSGA-II algorithm), which integrates heuristic initialization, adaptive evolution, and diversity maintenance mechanisms, is proposed to achieve joint optimization of repair teams and equipment reinforcement plans. A typical 220 kV substation case study demonstrates that the framework is feasible under the analyzed scenarios and exhibits better empirical search performance compared to the selected benchmark algorithm. Due to the limitations of a single topology and certain fixed input parameters, the obtained results are scenario-dependent and cannot be used to infer general applicability or global convergence.