A single-phase-to-ground fault location method based on convolutional deep belief network

Abstract

After single-phase-to-ground fault (SPGF) occurs in the resonant grounding system, it will produce transient electrical quantities with abundant characteristics, such as transient zero-sequence current, transient three-phase current, and transient zero-sequence voltage. At this point, there is a great discrepancy between transient zero-sequence current and transient three-phase current sudden-change at upstream and downstream detection nodes, while those at detection nodes on the same side are similar. Besides, the trend of zero-sequence current at upstream detection nodes and zero-sequence voltage is opposite, while the trend of them downstream is similar. On this basis, this paper presents a new method for locating SPGF, which is based on the convolutional deep belief network (CDBN). Firstly, transient zero-sequence voltage, transient zero-sequence current, and transient three-phase current sudden-change are decomposed and reconstructed by discrete wavelet packet transform (DWPT) to obtain a time-frequency matrix. Then, taking it as input of CDBN to autonomously extract fault characteristics, and fault detection nodes are divided into upstream and downstream detection nodes. Finally, combined with network topology, fault location can be realized. The simulation test and data analysis show this method can identify fault segments accurately and reliably, and has good adaptability and application value.