【摘要】 与简单随机采样相结合的蒙特卡罗(Monte Carlo)模拟法只有在采样规模足够大时才能得到高精度的计算结果,计算量较大。文中提出了用拉丁超立方采样和Gram-Schmidt序列正交化方法改善采样值对输入随机变量的分布空间的覆盖程度、提高采样效率的计算方法,并应用于概率潮流计算中。IEEE14节点系统和IEEE118节点系统的算例验证了该方法的有效性。与传统方法相比,文中所述方法可以降低采样规模,更有效地估计输出随机变量的统计参数和概率分布,同时保留蒙特卡罗模拟法的优点,在处理相同随机变量的一系列随机问题时可以显著降低计算量。更多还原

【Abstract】 Monte Carlo simulation method combined with simple random sampling is easy to use;and higher accuracy can be obtained only with a large enough sample size.Consequently,in order to achieve lowers error,the computational speed will have to be reduced and the computational costs remain very high.An effective sampling method,Latin hypercube sampling,is integrated into the probabilistic load flow calculation to increase the sampling efficiency of Monte Carlo simulation by improving the sample values coverage of random variables input spaces.The effectiveness and efficiency of the proposed method is proven by the comparative tests in the IEEE 14-bus system and IEEE 118-bus system.Compared with simple random sampling,it needs a much smaller sampling number to get a specified accuracy,and can effectively evaluate the statistical parameters and probability distribution of output stochastic variables.At the same time,the advantages of Monte Carlo simulation are preserved,and computational cost is dramatically reduced when dealing the stochastic problems of the same stochastic variables.更多还原