At present, the existing line selection method using single fault feature is affected by the transition resistance, fault closing angle and fault location, which generally has the problems of poor noise resistance and low accuracy. In view of this, a new small current grounding line selection method of extreme learning machines (ELM) is proposed, which integrates transient high frequency energy and waveform correlation fault characteristics. The transient high frequency energy in zero sequence current is extracted by using variable mode decomposition and hilbert transform. Through zero-sequence current correlation analysis, the comprehensive correlation coefficient of transient waveform is extracted, and the fault feature vector is formed by the two. The threshold free ELM model is input to realize fault line selection. The effectiveness of the proposed method is verified by extensive simulations, which show that the method is noise-resistant and largely unaffected by factors such as transition resistance, fault closure angle and fault location. The method is verified by the example of substation ground fault data in the paper, and the results show that the method has an accuracy of 100% in line selection.