Aiming at the problem that the traditional voltage sag source identification method has long classification time and low accuracy

a voltage sag source identification method based on generalized S transform（ GST） and genetic algorithm（ GA） to optimize the extreme learning machine is proposed. Firstly

the model matrix of generalized S transform is used to extract the feature quantities such as start and stop time

sag depth and phase jump of voltage sag. Then the genetic algorithm is used to optimize the input weight and hidden layer threshold of ELM to build voltage sag source recognition model based on GA-ELM

and realize voltage sag source recogmition.By MATLAB/SIMULINK simulation

compare the identification results of GA-ELM

ELM and BP neural networks to identify the voltage sag source. It is verified that the accuracy of voltage sag source identification using genetic algorithm to optimize ELM is higher than that of original ELM and BP neural networks.