Fault Detection Algorithm for Power Distribution Network Based on Sparse Self-Encoding Neural Network

With the future development of substation, the research of power fault detection algorithm has very important theoretical significance and wide application prospects. In order to improve the recognition of power line fault detection, one modeling method based on sparse self-encoding neural network is proposed. The dB3 wavelet is used to decompose the fault signal, and then the sub-band energy is calculated as parameters for the deep learning neural network. By the pre-training analysis and modeling for the characteristic of fault signal, the deep learning neural network is used as the fault recognition classifier. The simulation experiment based on IEEE 34 shows that the fault recognition rate exceeds 99%.