• Traditional reliability evaluation approaches, e.g., Monte-Carlo simulation (MCS) and analytical approaches, rely on scenario calculations and thus fail to meet the requirement of rapidity. • Compared with traditional reliability evaluation approaches, the data-driven model can accelerate the calculation speed but lacks interpretability. • During operational reliability evaluation, a subset of critical samples can significantly impact system reliability, whereas some samples have negligible effects, from which reliability-related knowledge can be derived. • The knowledge-informed data-driven approach embedded with reliability-related knowledge and power system physical knowledge can enhance interpretability and accuracy significantly.
Ni et al. (Fri,) studied this question.
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