Open AccessANN‐based robust DC fault protection algorithm for MMC high‐voltage direct current grids
Author(s) -
Xiang Wang,
Yang Saizhao,
Wen Jinyu
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2019.0733
Subject(s) - robustness (evolution) , artificial neural network , dc bias , algorithm , engineering , noise (video) , computer science , electronic engineering , modular design , time domain , control theory (sociology) , grid , voltage , electrical engineering , artificial intelligence , mathematics , biochemistry , chemistry , control (management) , image (mathematics) , computer vision , gene , operating system , geometry
Fast and reliable protection is a significant technical challenge in the modular multilevel converter (MMC)‐based DC grids. The existing fault detection methods suffer from the difficulty in setting protective thresholds, incomplete function, insensitivity to high‐resistance faults and vulnerable to noise. This study proposes an artificial neural network (ANN)‐based method to enable DC bus protection and DC line protection for DC grids. The transient characteristics of DC voltages are analysed during DC faults. On the basis of the analysis, the discrete wavelet transform is used as an extractor of distinctive features at the input of the ANN. Both frequency‐domain and time‐domain components are selected as input vectors. A large number of offline data considering the impact of noise is employed to train the ANN. The outputs of the ANN are used to trigger the DC line and DC bus protections and select the faulted poles. The proposed method is tested in a four‐terminal MMC‐based DC grid under PSCAD/EMTDC. The simulation results verify the effectiveness of the proposed method in fault identification and the selection of the faulty pole. The intelligent algorithm‐based protection scheme has good performance concerning selectivity, reliability, robustness to noise and fast action.