为了分析不确定性因素对电力系统的影响，兼具快速性和准确性的多项式混沌逼近法（polynomial chaos approximation, PCA）被广泛应用于概率潮流计算中。多项式混沌逼近法要求已知随机输入变量的概率密度函数（probability density function, PDF），同时随机输入变量需要满足独立条件。针对已知随机输入变量为历史数据的情况，提出了一种数据驱动型多项式混沌逼近（data driven polynomial chaos approximation, DDPCA）的概率潮流方法。首先，DDPCA根据历史数据选择最优的正交多项式，进而确定考虑随机输入变量非线性相关性时的高斯样本，然后结合蒙特卡洛积分计算权重。紧接着，基于高斯样本进行少量的潮流计算，并根据潮流结果和权重求解逼近系数，进而求取随机输出变量的统计特征。将所提方法与点估计法进行了比较，在三个算例上的结果验证了所提方法的有效性。

In order to analyze the influence of uncertain factors on power system, PCA (polynomial chaos approximation) method, which is both fast and accurate, is widely used in probabilistic power flow calculation. The polynomial chaotic approximation method requires that the probability density function of the random input variable is known, and the random input variable must satisfy the independent condition. A probabilistic power flow method based on DDPCA (data driven polynomial chaos approximation) was proposed for the known random input variables which are historical data. First, DDPCA selects the optimal orthogonal polynomial according to the historical data, and then determines the Gaussian sample considering the nonlinear correlation of random input variables, and then computes the weights with Monte Carlo integral. Then, a small amount of power flow was calculated based on Gaussian samples, and the approximation coefficient was solved according to the power flow results and weights, and then the statistical characteristics of the random output variables were obtained. The proposed method was compared with the point estimation method, and the effectiveness of the proposed method was verified by the results of three examples.