Open AccessIntelligent fault detection and location scheme for modular multi‐level converter multi‐terminal high‐voltage direct current
Author(s) -
Yang Qingqing,
Li Jianwei,
Santos Ricardo,
Huang Kaijia,
Igic Petar
Publication year - 2021
Publication title -
high voltage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.732
H-Index - 20
ISSN - 2397-7264
DOI - 10.1049/hve2.12033
Subject(s) - modular design , robustness (evolution) , computer science , recurrent neural network , feature extraction , artificial neural network , fault detection and isolation , pattern recognition (psychology) , boosting (machine learning) , high voltage direct current , artificial intelligence , support vector machine , voltage , control theory (sociology) , direct current , electronic engineering , engineering , electrical engineering , control (management) , biochemistry , chemistry , actuator , gene , operating system
In order to overcome the drawbacks of the conventional protection methods in high‐voltage direct current transmission lines, a deep learning approach is proposed that directly learn the fault conditions based on unsupervised feature extraction to the detection and location decision by leveraging the hidden layer activations of recurrent neural network. The deep‐recurrent neural network boosting with the gated recurrent unit compared with the long short‐term memory unit is used by analysing both the signal presented in time domain and frequency domain. The proposed method is tested based on a modular multilevel converter based four‐terminal high‐voltage direct current system. Various faults under different conditions were simulated against fault resistance, external faults and small disturbance immunity with the validity, and the simulation verified a high accuracy, robustness and fast results because of the utilization of characteristic feature extraction.