Liang Yongchao

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%.

Analyses and comparisons of SCL files in substation configurator

In order to improve the efficiency of electric system and reduce the works of maintenance, scholars from different countries have studied the substation automation. Nowadays, IEC61850 standard is the main rule to regulate the communication between substations. The paper studies the way to use substation configuration description language to describe models of IEC61850, compares the differences of SCL (substation configuration description language) file and discusses the rules of substation configuration description language (SCL) files in actual substation project.