Open AccessStacked denoising autoencoder based fault location in voltage source converters‐high voltage direct current
Author(s) -
Luo Guomin,
Cheng Mengxiao,
Hei Jiaxin,
Wang Xiaojun,
Huang Weibo,
He Jinghan
Publication year - 2021
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12115
Subject(s) - fault (geology) , autoencoder , high voltage direct current , overhead (engineering) , computer science , noise reduction , transmission (telecommunications) , voltage , direct current , transmission system , pattern recognition (psychology) , electronic engineering , artificial intelligence , engineering , artificial neural network , real time computing , electrical engineering , telecommunications , seismology , geology , operating system
High voltage direct current has been more and more popular in modern transmission systems. Accurate fault location could help fault clearance and fast recovery of the faulted system. A stacked denoising autoencoder based fault location method for high voltage direct current transmission systems is proposed. The local measurements are analysed, and an end‐to‐end stacked denoising autoencoder‐based fault location is realised. Representative features are extracted with unsupervised learning and labelled as the input of the regression network for fine‐tuning in a supervised manner. The trained network can precisely map the local measurements and their corresponding fault distance. The performance of the proposed method is tested on a point‐to‐point high voltage direct current transmission system, which is modelled on the platform of PSCAD/EMTDC. The faults on both overhead lines and cables are considered, and the location performance in different scenarios are discussed. The simulation results show that the proposed method is effective in pinpointing faults location in various cases.