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A novel approach based on EEMD sample entropy to fault current identification in DC traction network
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A novel approach based on EEMD sample entropy to fault current identification in DC traction network
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A novel approach based on EEMD sample entropy to fault current identification in DC traction network
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Journal Article
A novel approach based on EEMD sample entropy to fault current identification in DC traction network
2017
Overview
Summary With the occurrence of the oscillation current (OC) in the direct current (DC) traction network of rail transit, malfunctions appear in the relay protection system frequently. For the purpose of improving the reliability of the protection system, more effective feature extraction methods are expected to be taken into consideration. Thus, in this paper, a novel approach to feature extraction is proposed to make a distinction between the short‐circuit fault current (FC) and the OC, combined ensemble empirical mode decomposition (EEMD) and sample entropy (SampEn). Firstly, on the basis of EEMD method, the feeder current signal is disassembled, and a range of intrinsic mode functions can be derived. Then the SampEn value of each intrinsic mode functions component is calculated. Finally, all the SampEn values are summed up to serve as the feature vector that involves information on the operation state of DC traction network. In accordance with the simulation results of typical feeder current signals, it is proved that the proposed method is capable of distinguishing the FC and OC effectively. The measured data calculation results show that the proposed method can control the misjudgment rate below 5%. Therefore, the proposed method can provide a good reference for identifying the operation state of the DC traction network.
Publisher
John Wiley & Sons, Inc
