A multi-model probabilistic framework to evaluate seismic resilience of UHV converter stations

Abstract

Ultra-high voltage converter stations are significant electric power infrastructures with a high electric energy load. To quantitatively evaluate the seismic and recovery capability of a UHV converter station, this paper proposes a multi-model probabilistic resilience assessment framework divided into four analysis modules for hierarchical evaluation. The conditional probability set of the functional state between network nodes is determined using a matrix-based approach. Taking connectivity and power transmission capacity into account, the functional reliability model was built through the improved Bayesian network. A multi-parameter quantification of the post-earthquake recovery process was carried out by constructing the stepped functional recovery function as well as the time-varying seismic resilience function. The economic loss assessment model was built from multiple aspects. Taking a typical ± 800 kV UHV converter station as a case study for seismic resilience analysis, the probabilistic resilience assessment was realized, and variation trends of the multi-parameter expected value were obtained by mathematical expectation. This article shows that taking measures to postpone the sensitive interval of the system helps to avoid the seismic active area. This avoidance of seismic activity is the objective of optimization. The proposed framework can serve as a reference for the operation and optimization of power systems.