The existing high voltage direct current (HVDC) fault detection methods have low sensitivity and are difficult to identify high resistance grounding faults. This paper proposes a HVDC transmission system fault detection method based on Improved Grey Wolf optimizer (GWO) optimized time convolutional network (TCN), The fault current signal collected by the rectifier side detection device is directly used as the input data of TCN, which overcomes the cumbersome process of fault signal processing. The ± 500 kV HVDC transmission line model is established by using Simulink simulation software, and the simulation experiments are carried out for different fault areas and fault types. The fault detection methods based on LSTM model, bilstm model and CNN model are compared. The test results show that gwo-tcn network can reliably and accurately select the fault pole and selection of HVDC transmission line when the transition resistance is up to 800 Ω.