A method is proposed to assess the power system transient stability using projection twin support vector machine with regularization(RPTSVM). The high dimensional binomial optimization problem of transient stability assessment(TSA)based on support vector machine is transformed into two low dimensional binomial optimization problems. The regularization term in the objective function of PTSVM is added to improve the stability of assessment. Firstly

a group of classification features are extracted from the power system operation parameters to build the original features set

such as feature of power system and the feature of projection energy function. The approach of feature selection is employed to evaluate the classification capability of the original features. Secondly

the optimal feature set is determined to effectively reflect the transient stability of the power system. Then

the features set are divided into stable classes and unstable classes based on RPTSVM. The best projection axis for stable classes and unstable classes are found

so that the class of stable are projected onto the projection hyper-plane of stable class and then clustered as much as possible. While the class of unstable is projected onto the class of stable projection super-plane as far as possible away from the cluster of stable clusters. The computation time of the transient stability assessment is reduced. At the same time

the genetic algorithm is used for parameter optimization and the accuracy of transient evaluation methods is improved. Finally

the simulated results of classic IEEE 145-bus system and China Southern power grid demonstrate the feasibility and validity of the proposed method.