VSC-HVDC Fault Location Method Based on Effective Feature and Depth Belief Network

Abstract

Aiming at the problems that most fault location methods are applied to the high voltage direct current (HVDC) transmission system, such as low location accuracy and vulnerability to noise, a voltage source converter (VSC)-HVDC fault location method based on effective feature and deep belief network is proposed. Firstly, the Karenbauer phase mode transform is used to decouple the original fault current data to eliminate the coupling effect between positive and negative lines. Then, Hilbert Huang (HH) transform is used to obtain six effective features of information from frequency domain and time domain analysis to fully characterize the system fault current signal. Finally, the extracted effective features are input into the depth belief network for learning, so as to estimate the fault location in the transmission line. VSC-HVDC system is built based on the MATLAB platform for experimental analysis. The results show that the fault location obtained by the proposed method has the smallest overall fault location error, and the maximum and minimum errors are divided into about 0.52 km and -0.95 km. Compared with other methods, it has certain advantages in fault location.