Xu, YijunKorkali, MertMili, Lamine M.Chen, Xiao2024-01-222024-01-22202097809981331331530-1605https://hdl.handle.net/10919/117514Risk assessment of power system failures induced by low-frequency, high-impact rare events is of paramount importance to power system planners and operators. In this paper, we develop a cost-effective multi-surrogate method based on multifidelity model for assessing risks in probabilistic power-flow analysis under rare events. Specifically, multiple polynomial-chaos-expansion-based surrogate models are constructed to reproduce power system responses to the stochastic changes of the load and the random occurrence of component outages. These surrogates then propagate a large number of samples at negligible computation cost and thus efficiently screen out the samples associated with high-risk rare events. The results generated by the surrogates, however, may be biased for the samples located in the low-probability tail regions that are critical to power system risk assessment. To resolve this issue, the original high-fidelity power system model is adopted to fine-tune the estimation results of low-fidelity surrogates by reevaluating only a small portion of the samples. This multifidelity model approach greatly improves the computational efficiency of the traditional Monte Carlo method used in computing the risk-event probabilities under rare events without sacrificing computational accuracy.Pages 3127-3136application/pdfenCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 InternationalConference proceedinghttps://doi.org/10.24251/hicss.2020.3812020-JanuaryMili, Lamine [0000-0001-6134-3945]