Abstract. When single-phase to ground fault occurs in small current grounding system, the zero-sequence transient current contains a lot of complex fault information. In order to extract characteristic from the zero-sequence current accurately as the criterion of fault line selection, this paper puts forward a method of fault line selection based on VMD energy entropy and optimized K-means clustering. Firstly, the zero-sequence current of each line is decomposed by VMD (variational mode decomposition, VMD), and each sub mode of the original current is obtained. Secondly, the energy entropy of each sub mode is calculated as the characteristic quantity of each line state. Finally, the optimized K-means clustering algorithm is used to cluster the fault feature to realize fault line selection. In this paper, Simulink is used to simulate the single-phase to ground fault in resonant grounded system and the results show that the method that has high accuracy can provide reference for practical engineering application. IntroductionIn recent years, there have been a lot of fault line selection methods, mainly divided into the line selection method applying the steady-state signal and the transient signal [1]. The transient current signal has large intensity, and is not affected by the arc suppression coil, so the method of line selection applying transient signal is more suitable for the identification of faults [2][3]. A method of Hilbert transform and probabilistic neural network routing is proposed in [4]. In the proposed method, it is shown that the zero-sequence transient current of each line is decomposed into a series of intrinsic mode functions by the empirical mode decomposition (EMD) and the intrinsic mode function energy is calculated as the input of PNN network to select the fault line. However, the empirical mode decomposition is prone to the phenomenon of modal aliasing, which cannot accurately extract the fault information [5].In view of the problem of mode mixing in EMD, the Hilbert transform is improved by ensemble empirical mode decomposition (EEMD), and the fault feature components are extracted to identify the fault line [6]. However, EEMD is affected by the addition of white noise amplitude and integration times, and the computational complexity is large. Variational modal decomposition [7] is a new signal processing method that decomposes the original signal into k finite bandwidths with different center frequencies. This method not only effectively avoids the modal aliasing problem of EMD, but also greatly reduces the computational complexity and improves the computational efficiency relative to EEMD. K-means algorithm is a widely used clustering method [8], usually in European distance as a similarity measure. The algorithm is simple and has a fast convergence rate.