Optimal seismic intensity measure selection for high voltage electrical substation equipment in various setup configurations

This paper investigates the optimal intensity measures (IMs) for the probabilistic seismic demand model of high-voltage electrical substation equipment. While numerous studies have examined suitable IMs for building and non-building structures, this paper is the first to address the topic in the context of electrical substation equipment considering dynamic interaction between interconnected equipment. For this purpose, four key apparatuses (surge arrester, current transformer, circuit breaker, and disconnect switch) are examined in stand-alone configuration, as well as connected configurations via bus-slider and S-shaped flexible strip connectors. The cloud analysis, utilizing 80 triaxial as-recorded ground motions, is performed on the three-dimensional numerical model of the equipment in three different configurations. The optimality of 56 scalar IMs is assessed using the metrics of practicality, efficiency, proficiency, goodness of fit, and sufficiency. The findings indicate that multi-mode spectral acceleration IMs, which incorporate both higher-order modes and the effective mass associated with each mode, are the optimal IMs for equipment within connected systems. For apparatuses in stand-alone configuration, spectral acceleration and velocity at the first mode are appropriate IMs. Moreover, comparing structure-dependent spectral IMs for apparatuses in connected configurations shows that using periods from the apparatuses in their stand-alone state improves the optimal values for the surge arrester and current transformer. In contrast, for the circuit breaker, the results are reversed, and for the disconnect switch, the choice between stand-alone or system-wide modal properties has little impact on the results. The sensitivity of optimal values to the assumed damping ratio in the spectral analysis highlights that the differences in results among 2 % (related to equipment) and 5 % (commonly used) are negligible. © 2024 Elsevier Ltd