With the vast integration of renewable energy, modern power systems are becoming large-scale networks with a greater number of uncertainty sources, which makes Probabilistic Optimal Power Flow (POPF) analysis quite time-consuming. On the one hand, the larger scale of a network makes the implementation of the Deterministic Optimal Power Flow (DOPF) more complicated; on the other hand, to obtain an accurate output, a heavier computation burden on DOPF is unavoidable due to the more uncertainty sources. Correspondingly, a Discrete Fourier Transformation Matrix (DFTM) is adopted to implement a probabilistic optimal power flow calculation, and the characteristics of DFTM samples are further investigated. The DFTM method is flexible in sampling point and can accurately handle the correlation amongst variables. Therefore, the DFTM method is able to balance the accuracy and efficiency of POPF analysis. Finally, the modified IEEE 118-bus system is adopted and the Monte Carlo simulation method is used as a reference to verify the effectiveness and superiority of the DFTM method in different proportion of asymmetrically distributed random variables. Compared with the unscented transformation method, the superiority of DFTM method is shown further. This work is supported by Science and Technology Project of State Grid Corporation of China (No. 5442DZ170034) and National Natural Science Foundation of China for International Cooperation and Exchanges (No. 5181101576).