Traditional transient stability assessment of power system is based on time domain simulation calculation

which has high computational complexity and is difficult to be applied online. An online transient stability assessment method based on one-dimensional convolutional neural network is proposed

which can greatly improve the speed of online transient stability assessment. Markov chain Monte Carlo sampling algorithm is used to simulate power system operation state and generate large-scale operation data. The maximum power angle difference of generator is determined by time domain simulation of power system. The operation data of power system is taken as the input of one-dimensional convolutional neural network

and the maximum power angle difference of generator is taken as the output to train the one-dimensional convolution neural network. In the online application scenario

one-dimensional convolutional neural network can quickly calculate the maximum power angle difference of generator based on the current operation data to realize online transient stability assessment. The New England 39 bus system verifies the feasibility of the proposed online evaluation algorithm.