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首页> 外文期刊>IEEE Transactions on Power Systems >A technique for reducing computational effort in Monte-Carlo based composite reliability evaluation
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A technique for reducing computational effort in Monte-Carlo based composite reliability evaluation

机译：一种基于蒙特卡洛的用于降低综合可靠性评估的计算量的技术

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摘要

The authors describe a novel technique for reducing the number of samplings in Monte-Carlo-based composite power system reliability evaluation. The proposed technique uses analytical information from simple models (such as a generation reliability model) as regression variables to reduce the variance of LOLP (loss-of-load probability) and EPNS (expected power not supplied) estimates with the complete (composite) model. Sample size reductions of up to two orders of magnitude were obtained in case studies with a 24 bus modified reliability test system and a 124 bus reduced Brazilian system. Speed-ups of 6.1 to more than 40 were obtained for the estimate of the EPNS and of 2.1 to 3.8 for the LOLP estimate. In all cases, the EPNS index required more samplings to converge to the required tolerance (5% relative uncertainty) than the respective LOLP index (for the same tolerance). As a consequence, the higher speedups prevail in the overall convergence if a uniform criterion is adopted.
机译：作者描述了一种新颖的技术，用于减少基于蒙特卡洛的复合电力系统可靠性评估中的采样次数。所提出的技术使用来自简单模型（例如发电可靠性模型）的分析信息作为回归变量，以减少具有完整（复合）模型的LOLP（负载损失概率）和EPNS（未提供预期功率）估计的方差。在使用24总线改进型可靠性测试系统和124总线简化型巴西系统的案例研究中，样本数量最多减少了两个数量级。对于EPNS的估算，速度提高了6.1至40倍；对于LOLP估算，速度提高了2.1至3.8。在所有情况下，与相应的LOLP指数（对于相同的公差）相比，EPNS指数需要更多的采样才能收敛到所需的公差（相对不确定度5％）。因此，如果采用统一的标准，则较高的加速比将占总体收敛速度。

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