Open AccessA Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System utilizing wavelet scattering transform
Author(s) -
Manohar Mishra,
Debadatta Amaresh Gadanayak,
Abha Pragati,
Jai Govind Singh
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3574222
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
This study presents a novel algorithm for automatic fault detection in multi-terminal voltage source converter-based high voltage direct current (MT-VSCHVDC) systems. The approach integrates the wavelet scattering transform (WST) to extract low-variance feature vectors and a newly developed variable batch size long-short-term-memory (VB-LSTM) network for accurate fault detection. The synthetic minority oversampling technique addresses class imbalance issues in fault and no-fault data. Utilizing a 5-fold cross-validation process, the study demonstrates that WST-based features, combined with the proposed VB-LSTM network and a hybrid training strategy, achieve 100% and 99.46% accuracy in classifying internal and external faults with no-noise and 20db noisy conditions respectively. The variable epoch size training enhances convergence speed, leading to more stable and consistent results. A secondary LSTM model with a similar layer architecture is also trained and evaluated on WST-based features to identify the fault location within internal faults. The proposed fault distance (WST-VB-LSTM) estimation model achieves excellent performance with an R-square of 0.9946 and MAE of 0.0135 without noise, and maintains robustness under noise with 30 dB (R² = 0.9903, MAE = 0.0182) and 20 dB (R² = 0.9598, MAE = 0.0364). Lastly, comparative performance analysis reveals that the proposed model outperforms recently published works and state-of-the-art techniques, exhibiting higher accuracy and significantly lower error metrics across varying noise conditions.