Intelligent high impedance fault detection method for distribution networks based on time-frequency diagram
When a high impedance fault occurs in a distribution network,traditional protection means cannot be effective due to the small fault current.When emerging detection methods perform feature extraction,artificially constructed feature quantities or feature vectors often have difficulty in fully representing the characteristics that distinguish high impedance fault from other events.To this end,this paper first converts zero-sequence currents into time-frequency diagrams using continuous wavelet transform(CWT),and then segments the images into square slices.A convolutional neural network(CNN)is used to identify the characteristic"tooth"feature of high impedance fault.The proposed method is able to visualize high-frequency components of the waveform by means of the time-frequency diagram,and its reliability has been verified in simulations and field sample tests.
distribution networkhigh impedance faulttime-frequency diagramimage segmentationdeep learningconvolutional neural network