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Title: An Efficient Multifidelity Model for Assessing Risk Probabilities in Power Systems under Rare Events
Risk 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.  more » « less
Award ID(s):
1917308 1711191
PAR ID:
10157224
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
53rd Hawaii International Conference on System Sciences 2020
Page Range / eLocation ID:
1-10
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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