In order to eliminate the influence of grounding mode, fault type and fault location on the accuracy of ground fault diagnosis in low current system. By analyzing the zero sequence current of all kinds of single-phase ground faults in this system, a single-phase ground fault detection method was proposed on the basis of the improved Hilbert-Huang transform (HHT) and Extreme learning machine (ELM). This method firstly used wavelet transform (WT) for multiband signal. Then HHT was performed on the characteristic signal that was selected by the charging and discharging characteristics of the ground capacitance to obtain the instantaneous energy of the zero sequence current of each line. Finally, gray wolf optimization (GWO) and particle swarm optimization (PSO) were used to optimize the ELM model to obtain the GWO-PSO-ELM model with fault type recognition and line selection functions. A fault detection system based on digital fault indicator (DFI) acquisition platform and master station data processor is designed. The test results show that this method can accurately judge the fault type and complete line selection, and the accuracy reaches more than 90%.