Series arc-fault diagnosis using convolutional neural network via generalized S-transform and power spectral density

It is difficult to identify an arc fault accurately when the loads on the user side are more complicated, which hinders the development of low-voltage monitoring and pre-warning inspection. This study acquired a series of arc-fault signals according to IEC 62606. The main time-frequency features were strengthened with high efficiency by applying the generalized S-transform to them with a bi-Gaussian window. Further, the power spectrum density determination allowed for the detection of imperceptible high-frequency harmonic energy reflections, thus increasing the rate of arc-fault diagnosis and making it suitable for arc-fault monitoring of non-linear loads. The final samples were trained and classified using a 2D convolutional neural network and the overall accuracy of identification was observed to be 98.13%, which involved various domestic loads, thus providing a reference for follow-up arc-fault monitoring and inspection research.