Application of Bayesian Compressed Sensing Theory in Single -Phase-to-Ground Fault Line Selection of Distribution Network

In order to obtain accurate single-phase-to-ground zero-sequence current signal to improve the accuracy of fault line selection, a new data acquisition method-compressed sensing theory, was applied to single-phase-to-ground fault line selection of distribution network. A new method of fault line selection based on Bayesian compressed sensing theory was proposed. The method used compressed sampling method to sample zero-sequence current of each line from 1/4 cycle before the fault to 1/4 cycle after the fault, and then reconstructed the zero-sequence current signal accurately by reconstruction algorithm. EEMD method was used to decompose the reconstructed signal, and then the relative energy factor of each line was calculated. The faulty line was selected by comparing the relative energy factor of each line. The simulation results showed that the proposed method was not limited by sampling frequency and has high accuracy and reliability. This method was suitable for cable-line hybrid lines, pure cable lines and pure overhead lines.