(1) establishing a simulation model of a HVDC bipolar transmission system based on a voltage source converter, and selecting an output voltage and current signals of a rectifier side bus under different fault types, fault distances and transition resistances as an original data set, and labeling classification of fault segments and labeling a location of a fault position according to the fault segments of a transmission line and its precise fault position where the fault occurs;

(2) performing variational modal decomposition (VMD) on the selected voltage and current on the rectifier side in various fault scenarios after phase-mode transformation, obtaining an effective intrinsic mode function (IMF) component of the signal, and calculating a Teager energy operator (TEO) of the IMF component to obtain a fault data set;

(3) performing normalized data preprocessing on the fault data set after performing VMD and TEO, and dividing the preprocessed fault data set into a training set and a test set;

(4) inputting the training set and the test set to a convolutional neural network (CNN)-long short-term memory (LSTM) network model in sequence for model training and test respectively, wherein the CNN is used as a classifier to identify the fault segments, and the LSTM network is used as a regressor to position faults of the HVDC transmission line with minimum effects cause by fault types, noise, sampling frequency and different HVDC topologies.