电力系统可靠性评估可量化元件故障对供电能力的影响，是系统规划、运行调度和风险管控的重要依据。状态枚举方法具有结果确定、可解释性强等优点，但受状态空间组合爆炸限制，大规模系统通常只能分析有限低阶状态，导致可靠性指标下界松弛并低估系统风险。为提高有限状态集合下可靠性指标下界的精度，提出一种基于失负荷量尾概率重构的可靠性下界评估方法。在失负荷量单调性工程假设下，该方法由已分析状态的故障元件集合和失负荷量推断相关高阶未分析状态的失负荷信息，并将可靠性指标由逐状态加权形式重构为失负荷阈值尾概率形式。进一步结合极小故障集化简、二叉决策图精确概率计算和增量更新机制，高效构造尾概率下界并计算可靠性指标。算例结果表明，在相同计算时间下，所提方法能够保持指标下界性质，并较传统状态枚举获得更准确的可靠性指标。

Power system reliability assessment quantifies the impact of component failures on supply capability, providing an important basis for system planning, operation scheduling, and risk management. State enumeration produces deterministic and interpretable results; however, due to the combinatorial explosion of the state space, only a limited number of low-order states can usually be analyzed in large-scale systems. This leads to loose lower bounds of reliability indices and underestimation of system risk. To improve the accuracy of reliability-index lower bounds under a finite set of analyzed states, this paper proposes a reliability lower-bound assessment method based on tail-probability reconstruction of load shedding. Under the engineering monotonicity assumption of load shedding, the proposed method infers load-shedding information of related higher-order unanalyzed states from the component-outage sets and load shedding of analyzed states, and reformulates reliability indices from a state-by-state weighted form into a tail-probability form over load-shedding thresholds. Furthermore, minimal failure-set simplification, binary decision diagram-based exact probability calculation, and an incremental updating mechanism are combined to efficiently construct tail-probability lower bounds and calculate reliability indices. Case studies show that, under the same computation time, the proposed method preserves the lower-bound property and obtains more accurate reliability indices than traditional state enumeration.