A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform
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...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11015988/ |
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| author | Manohar Mishra Debadatta Amaresh Gadanayak Abha Pragati Jai Govind Singh |
| author_facet | Manohar Mishra Debadatta Amaresh Gadanayak Abha Pragati Jai Govind Singh |
| author_sort | Manohar Mishra |
| collection | DOAJ |
| description | 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 (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =0.9903$ </tex-math></inline-formula>, MAE =0.0182) and 20 dB (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =0.9598$ </tex-math></inline-formula>, 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. |
| format | Article |
| id | doaj-art-2f41ff3d2ae14b82935eb4e14789c040 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2f41ff3d2ae14b82935eb4e14789c0402025-08-20T02:32:10ZengIEEEIEEE Access2169-35362025-01-0113956479566410.1109/ACCESS.2025.357422211015988A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering TransformManohar Mishra0https://orcid.org/0000-0003-2160-4703Debadatta Amaresh Gadanayak1https://orcid.org/0000-0001-7533-4441Abha Pragati2Jai Govind Singh3https://orcid.org/0000-0002-0162-3360Department of Electrical and Electronics Engineering, Siksha O Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, IndiaDepartment of Electrical and Electronics Engineering, Siksha O Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, IndiaDepartment of Electrical and Electronics Engineering, Siksha O Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, IndiaDepartment of Energy and Climate Change, School of Environmental Resource and Development, Asian Institute of Technology, Khlong Luang, Bangkok, Pathum Thani, ThailandThis 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 (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =0.9903$ </tex-math></inline-formula>, MAE =0.0182) and 20 dB (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =0.9598$ </tex-math></inline-formula>, 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.https://ieeexplore.ieee.org/document/11015988/HVDC protectionScatterNet application in HVDC protectionwavelet scattering transform for HVDC protectionfault recognition in HVDCfault location in HVDC |
| spellingShingle | Manohar Mishra Debadatta Amaresh Gadanayak Abha Pragati Jai Govind Singh A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform IEEE Access HVDC protection ScatterNet application in HVDC protection wavelet scattering transform for HVDC protection fault recognition in HVDC fault location in HVDC |
| title | A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform |
| title_full | A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform |
| title_fullStr | A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform |
| title_full_unstemmed | A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform |
| title_short | A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform |
| title_sort | deep learning approach for fault detection and localization in mt vsc hvdc system utilizing wavelet scattering transform |
| topic | HVDC protection ScatterNet application in HVDC protection wavelet scattering transform for HVDC protection fault recognition in HVDC fault location in HVDC |
| url | https://ieeexplore.ieee.org/document/11015988/ |
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