中文摘要：

系统状态随机抽样是大电网可靠性蒙特卡洛仿真的重要基础环节，抽取的样本容量与仿真结果的精度和仿真耗时密切相关，因此在给定仿真精度时实现样本容量的概率预估和在给定样本容量时实现计算精度的概率预测，是实现计算精度和计算成本综合权衡的关键。基于中心极限定理深入研究随机变量的样本均值与期望值之间误差的概率预测方法，在此基础上分析失负荷概率（loss of load probability，LOLP）指标的方差系数和样本容量之间的关系表达式，导出方差系数给定时的样本容量置信区间公式及样本容量给定时的方差系数置信区间公式。这些公式的导出，对于实现仿真精度和样本容量之间的定量概率分析具有重要意义，通过对RBTS和RTS96可靠性测试系统的评估分析验证所提方法的有效性和正确性，并得出相关结论。

英文摘要：

Random sampling of system state is the fundamental procedure in Monte-Carlo simulation of bulk power system reliability evaluation, and the sampling size has significant effects on simulation accuracy and calculation time. So the probabilistic forecasting technique for simulation accuracy and sampling size is the key to balance the calculation accuracy and cost. The probabilistic forecasting method of the error between the sample mean and expectation value of the random variable utilizing the central limit theory was researched, then the mathematical formula for the relation between sampling size and variance coefficient of loss of load probability （LOLP） was analyzed. Furthermore, the formulas for the relation between confidence intervals of variance coefficient and sampling size are deduced. These formulas are valuable to realize the probabilistic quantitative analysis between simulation accuracy and sampling size. Finally, the RBTS and IEEE-RTS96 power systems are evaluated to verify their validity, and some valuable conclusions are drawn.