Arc fault detection based on dual-channel time-frequency convolutional neural network
The high temperature generated by the AC fault arc can easily ignite the surrounding combustible materials,which is one of the important causes of wire fires.Accurate detection of different types of fault arcs is of great significance to avoid major fire accidents.However,the complexity and concealment of arc faults bring great challenges to detection methods.Techniques based on threshold and electric current feature extrac-tion are difficult to comprehensively generalize the characteristics of arc faults,while most methods based on deep neural networks directly perform feature learning on current signals and ignore the frequency information in the signal,resulting in poor generalization.In this regard,this paper proposes a fault arc identification method with a two-channel time-frequency convolutional neural network based on time-frequency feature learning,and designs a learnable discrete wavelet transform to extract multi-scale features in one-dimensional signals.Meanwhile,time fourier transform is employed to obtain two-dimensional time-frequency feature.Then convolutions are performed on those two channels'features,respectively for further feature fusion and prediction.Experiments are carried out on the arc current signals obtained under the three types of working conditions to verify the effectiveness of the proposed method.The experimental results show that the method has higher arc recognition accuracy compared with several competitive methods,reaching 97.91%.
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