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中文摘要：
概率潮流（probabilistic load flow，PLF）计算是电力系统稳态运行分析的重要工具。传统半不变量法概率潮流（PLFbased on cumulant method，PLF-CM）要求各输入变量相互独立，这使其不能直接应用于输入变量具有相关性的场合。针对这一情况，提出一种基于Cholesky分解的计及输入变量相关性的PLF．CM计算方法。同时，为解决一些输入变量的半不变量难以被常规数值方法求解的问题，提出基于蒙特卡罗抽样的方法，该方法利用输入变量的样本计算其半不变量。对改进的IEEE14节点系统进行仿真计算，结果验证了所提方法的有效性、准确性和实用性。在此基础上利用所提方法分析了风速相关性对系统运行特性的影响。结果表明系统运行特性受风速相关性影响较大。
英文摘要：
Probabilistic load flow （PLF） calculation is an important tool for system steady state performance analysis. Traditional PLF based on cumulant method （PLF-CM） requires that the input variables should be independent, so it can＇t be directly applied to the circumstance in which the input variables are correlated. Therefore, a novel PLF-CM considering the correlation between input variables based on Cholesky decomposition was proposed. In order to solve the problem that the cumulants of some input variables are hard to be obtained by conventional numerical method, a method based on Monte Carlo sampling was proposed, which calculated the cumulants of input variable by its sample. The modified IEEE 14-bus system was used in the simulation. The simulation results verified the effectiveness, accuracy and practicability of the propose method. The impacts of wind speed correlation （WSC） on power system operation characteristic were investigated by the proposed method and the results show that the system operation is affected by WSC.
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