Fault Identification Technology of Series Arc Based on Deep Learning Algorithm

At present, protection devices such as low-voltage circuit breakers and fuses are commonly used in low-voltage distribution networks, which can effectively prevent short circuits, overloads, and ground leakage. However, this method is out of work in detecting series arc faults caused by poor contact, insulation failure, etc. Therefore, how to achieve accurate detection of series arc faults has become a hot issue in current research. Wavelet transform is usually used for series arc fault detection. But it exists the problem of spectral aliasing, the false detection rate is still high. This paper uses detection method based on the current waveform to carry out research. By building an arc fault platform to simulate series arc faults, normal and arc fault data under different loads have been obtained. The structure of deep learning algorithm can be established through these experimental data. The accuracy of the algorithm is improved by using mini-batch gradient descent, exponential decay learning rate and Adam's optimization algorithm. By establishing test data for diagnostic verification, it was found that the algorithm has an excellent recognition rate.