Low-voltage distribution lines may produce arc faults,which may cause circuit faults.To distinguish between normal and fault currents,a fault arc detection method based on two-dimensional current images and convolutional neural networks is proposed.Firstly,the current signals are reconstructed using variational mode decomposition to address the challenge of distinguishing between normal and fault currents in nonlinear loads.Then,the Markov transition field algorithm is utilized to encode the reconstructed current signals into two-di-mensional images,generating a dataset of feature images.To enhance the accuracy and efficiency of fault arc detection,a CNN-based fault diagnosis model is constructed.The proposed feature image dataset and the data-set of feature images without signal reconstruction are respectively fed into the constructed diagnostic model for comparison and validation.Results indicate that the proposed method effectively mitigates the confusion caused by nonlinear load states,achieving an average detection accuracy of 99%.
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