The uncertainty of wind power makes available transfer capability(ATC)calculation more difficult. A fast ATC calculation method under certain limit violation probability request is proposed based on the point estimate method

Gram-Charlier series expansion theory and deep learning. The types of constraints considered include static security

static voltage stability and transient stability constraints. Assuming the probability distribution function of wind power is known

the cumulative distribution function of total transfer capability(TTC)is approximated through the TTC results of two deterministic operation scenes according to two-point estimate method and Gram-Charlier series expansion. In order to calculate TTC of the deterministic operation scene fast and accurately

a fast TTC calculation model based on stacked denoising autoencoder(SDAE)is developed. After the cumulative distribution function of TTC is known

the limit violation probability is defined as the probability that the power of flowgate is more than TTC and the calculation formula of ATC under certain limit violation probability is derived. Experiment results of a real power system demonstrate that the proposed method is able to consider multiple security and stability constraints effectively and calculate ATC under different limit violation probability requests fast and accurately.