中文摘要：

元件稳态可用度是电力系统可靠性评估中的重要数据。传统的可用度估计在样本信息不充足的情况下的结果不再准确。现有的样本信息和完整的概率信息之间的差距会产生非精确性。在这种情况下可以用非精确概率代替传统的精确概率，为非精确性和随机性建模。该文采用伽玛指数模型构造无故障工作时间和故障修复时间的概率箱，进而得到元件稳态可用度区间值；分析参数S对稳态可用度区间值收敛速度的影响。该稳态可用度区间值依概率收敛到真实值。这个区间值大小反映随机性，区间宽度反映非精确性。算例分析说明了所提方法在实际电力系统中的应用；并利用马尔科夫更新过程模拟生成样本数据，验证了算法的有效性。

英文摘要：

Steady-state availabilities of components are important statistical data in power system reliability evaluation. If sample information is severely incomplete, the traditional estimation of steady-state availability is not accurate. The gap between existing sample information and complete probability information causes imprecision. In such case, imprecise probability can substitute for traditional precise probability and be used to build models for both imprecision and randomness. In this paper, gamma-exponential model was used to build probability boxes of time to failure and time to repair, and then interval-valued steady-state availabilities of components were calculated. The effect of parameter s on the convergence speed of interval-valued steady-state availability was also analyzed. The interval-valued steady-state availability converges to the true value of availability. The value of the interval can reflect randomness, and the width of the interval can reflect imprecision. Numerical example illustrated the practical application of the proposed method; the validity of the algorithm was verified by using Markov renewal process to produce the sample data.