The large-scale integration of wind power has increased the uncertainty in power systems. Probabilistic load flow calculation is a significant probabilistic analysis tool. Based on generalized polynomial chaos(gPC)

a method is proposed to calculate probabilistic load flow with wind generation uncertainties. With this method

each random input variable is approximated by a finite-order expansion using optimal gPC basis functions. Then

a series of deterministic nonlinear equations can be built according to the orthogonality of gPC basis functions. Thus

the solution of random state variables' distribution is transformed into calculation of their gPC approximation coefficients. In addition

the idea of equivalent transformation is adopted to deal with the complexity of wind power probabilistic distribution and correction of wind speed. Calculation on an IEEE 30 bus system suggests that the proposed method can achieve a good degree of accuracy as well as higher computational efficiency in contrast to Monte Carlo simulation(MCS)method.