基于TPNT和半不变量法的考虑输入量相关性概率潮流算法

A TPNT and cumulants based probabilistic load flow approach considering the correlation variables

电力系统的不同输入量之间具有相关性,会影响概率潮流计算的准确性。提出基于三阶多项式正态变换方法处理随机变量相关性,结合半不变量与Gram-Charlier级数展开的概率潮流算法。该方法能够将相关非正态的多维随机变量变换到正态不相关的变量空间,得到相关系数矩阵处理相关输入量,然后进行概率潮流计算,得到节点状态变量和支路潮流功率的潮流计算结果和概率分布曲线。对含多个风速分布相关风电场的IEEE14节点系统进行分析计算,结果验证了该方法的有效性和准确性。

The correlation among input variables in power system affects the accuracy of probabilistic load flow （PLF） calculation. A PLF algorithm is proposed based on third-order polynomial normal transformation （TPNT） combining cumulants and Gram-Charlier expansion. The method that transforms a multivariate non-normal dependent random variables group into a multivariate standard normal independent one is used to calculate input variables and power flow based on correlation coefficient matrix and draw cumulative distribution curves of node status variables and load flow. The simulation results of the IEEE14-bus system with several correlative wind farms show that the proposed method is effective and accurate.