Graph Convolutional Network Based Fault Detection and Identification for Low-voltage DC Microgrid
This paper presents a novel fault detection and identification method for low-voltage direct current (DC) microgrid with meshed configuration. The proposed method is based on graph convolutional network (GCN), which utilizes the explicit spatial information and measurement data of the network topolo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9910360/ |
| _version_ | 1797818691526590464 |
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| author | Ambuj Pandey Soumya R. Mohanty |
| author_facet | Ambuj Pandey Soumya R. Mohanty |
| author_sort | Ambuj Pandey |
| collection | DOAJ |
| description | This paper presents a novel fault detection and identification method for low-voltage direct current (DC) microgrid with meshed configuration. The proposed method is based on graph convolutional network (GCN), which utilizes the explicit spatial information and measurement data of the network topology to identify a fault. It has a more substantial feature extraction ability even in the presence of noise and bad data. The adjacency matrix for GCN is developed by considering the network topology as an inherent graph. The bus voltage and line current samples after faults are regarded as the node attributes. Moreover, the DC microgrid model is developed using PSCAD/EMTDC simulation, and fault simulation is carried out by considering different possible events that include environmental and physical conditions. The performance of the proposed method under different conditions is compared with those of different machine learning techniques such as convolutional neural network (CNN), support vector machine (SVM), and fully connected network (FCN). The results reveal that the proposed method is more effective than others at detecting and classifying faults. This method also possesses better robustness under the presence of noise and bad data. |
| first_indexed | 2024-03-13T09:11:53Z |
| format | Article |
| id | doaj.art-25818f4dc0e54c5fa8a286531f375548 |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-03-13T09:11:53Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-25818f4dc0e54c5fa8a286531f3755482023-05-26T23:01:13ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202023-01-0111391792610.35833/MPCE.2022.0002519910360Graph Convolutional Network Based Fault Detection and Identification for Low-voltage DC MicrogridAmbuj Pandey0Soumya R. Mohanty1Indian Institute of Technology (BHU),Department of Electrical Engineering,Varanasi,Uttar Pradesh,IndiaIndian Institute of Technology (BHU),Department of Electrical Engineering,Varanasi,Uttar Pradesh,IndiaThis paper presents a novel fault detection and identification method for low-voltage direct current (DC) microgrid with meshed configuration. The proposed method is based on graph convolutional network (GCN), which utilizes the explicit spatial information and measurement data of the network topology to identify a fault. It has a more substantial feature extraction ability even in the presence of noise and bad data. The adjacency matrix for GCN is developed by considering the network topology as an inherent graph. The bus voltage and line current samples after faults are regarded as the node attributes. Moreover, the DC microgrid model is developed using PSCAD/EMTDC simulation, and fault simulation is carried out by considering different possible events that include environmental and physical conditions. The performance of the proposed method under different conditions is compared with those of different machine learning techniques such as convolutional neural network (CNN), support vector machine (SVM), and fully connected network (FCN). The results reveal that the proposed method is more effective than others at detecting and classifying faults. This method also possesses better robustness under the presence of noise and bad data.https://ieeexplore.ieee.org/document/9910360/DC microgridgraph convolution networkfault detectiontopological information |
| spellingShingle | Ambuj Pandey Soumya R. Mohanty Graph Convolutional Network Based Fault Detection and Identification for Low-voltage DC Microgrid Journal of Modern Power Systems and Clean Energy DC microgrid graph convolution network fault detection topological information |
| title | Graph Convolutional Network Based Fault Detection and Identification for Low-voltage DC Microgrid |
| title_full | Graph Convolutional Network Based Fault Detection and Identification for Low-voltage DC Microgrid |
| title_fullStr | Graph Convolutional Network Based Fault Detection and Identification for Low-voltage DC Microgrid |
| title_full_unstemmed | Graph Convolutional Network Based Fault Detection and Identification for Low-voltage DC Microgrid |
| title_short | Graph Convolutional Network Based Fault Detection and Identification for Low-voltage DC Microgrid |
| title_sort | graph convolutional network based fault detection and identification for low voltage dc microgrid |
| topic | DC microgrid graph convolution network fault detection topological information |
| url | https://ieeexplore.ieee.org/document/9910360/ |
| work_keys_str_mv | AT ambujpandey graphconvolutionalnetworkbasedfaultdetectionandidentificationforlowvoltagedcmicrogrid AT soumyarmohanty graphconvolutionalnetworkbasedfaultdetectionandidentificationforlowvoltagedcmicrogrid |