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A current identification method based on optimized variational mode decomposition (VMD) and sample entropy (SampEn) is proposed in order to solve the problem that the main protection of the urban rail transit DC feeder cannot distinguish between train charging current and remote short circuit current. This method uses the principle of energy difference to optimize the optimal mode decomposition number  k  of VMD; the optimal VMD for DC feeder current is decomposed into the intrinsic modal function (IMF) of different frequency bands. The sample entropy algorithm is used to perform feature extraction of each IMF, and then the eigenvalues of the intrinsic modal function of each frequency band of the current signal can be obtained. The recognition feature vector is input into the support vector machine model based on Bayesian hyperparameter optimization for training. After a large number of experimental data are verified, it is found that the optimal VMD_SampEn algorithm to identify the train charging current and remote short circuit current is more accurate than other algorithms. Thus, the algorithm based on optimized VMD_SampEn has certain engineering application value in the fault current identification of the DC traction feeder.

Fault Current Identification of DC Traction Feeder Based on Optimized VMD and Sample Entropy