Observational analysis shows improved convergence in reliability evaluation using a multi-objective cross-entropy approach for optimizing sampling methods.
Reliability evaluation of power distribution systems is computationally intensive, as standard Monte Carlo simulations require extensive sampling to accurately estimate rare event-based indices like SAIDI and SAIFI. This paper introduces a multi-objective cross-entropy approach for reliability evaluation of power distribution systems, aiming to accelerate reliability evaluation by optimizing importance sampling reference parameters. The multi-objective approach aims to optimize a set of objective functions related to systemic and load point reliability indices. A deduction of an analytical solution for the optimization of reference parameters of the cross-entropy method is developed, taking into account the standard hypotheses used in reliability assessments. The proposed method has been validated on a real 181-node Brazilian distribution feeder. Results show that the proposed approach can accelerate the convergence of estimates for reliability indices in comparison with the crude Monte Carlo approach and the single-objective CE method.
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Venturini et al. (2025) studied this question.
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