Research on Time-frequency Analysis and Identification of Series Arc Fault Based on Generalized S-transform
It is difficult to identify the arc fault effectively when the loads on the user side have become increasingly complex,which blocks the development of fault monitoring and pre-warning inspection.In this paper,the time-frequency analysis and identification of series arc fault was studied based on the generalized S-transform.Firstly,the differences in time-frequency features of arc faults among 3 signal processing methods,STFT(Short-time Fourier Transform),wavelet transform and generalized S-transform were compared,highlighting the advantages of generalized S-transform in processing high-frequency features of nonlinear loads.After that,the bi-Gaussian generalized S-transform was used to receive time-frequency features of the nonlinear loads and construct image feature samples.Finally,the samples are trained and classified by 2D-CNN(two-dimensional Convolutional Neural Network),and the recognition effectiveness was verified by the accuracy and clustering analysis.The overall accuracy is 96.81％,of which involves various domestic loads,providing a reference for the follow-up arc fault monitoring and inspection research.
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