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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.16522 |
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| _version_ | 1866911766015901696 |
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| author | Pei, Hao Lin, Si Li, Chuanfu Wang, Che Chen, Haoming Li, Sizhe |
| author_facet | Pei, Hao Lin, Si Li, Chuanfu Wang, Che Chen, Haoming Li, Sizhe |
| contents | To enhance the intelligence degree in operation and maintenance, a novel method for fault detection in power grids is proposed. The proposed GNN-based approach first identifies fault nodes through a specialized feature extraction method coupled with a knowledge graph. By incorporating temporal data, the method leverages the status of nodes from preceding and subsequent time periods to help current fault detection. To validate the effectiveness of the node features, a correlation analysis of the output features from each node was conducted. The results from experiments show that this method can accurately locate fault nodes in simulation scenarios with a remarkable accuracy. Additionally, the graph neural network based feature modeling allows for a qualitative examination of how faults spread across nodes, which provides valuable insights for analyzing fault nodes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_16522 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Dynamic Fault Characteristics Evaluation in Power Grid Pei, Hao Lin, Si Li, Chuanfu Wang, Che Chen, Haoming Li, Sizhe Machine Learning Computation and Language Signal Processing To enhance the intelligence degree in operation and maintenance, a novel method for fault detection in power grids is proposed. The proposed GNN-based approach first identifies fault nodes through a specialized feature extraction method coupled with a knowledge graph. By incorporating temporal data, the method leverages the status of nodes from preceding and subsequent time periods to help current fault detection. To validate the effectiveness of the node features, a correlation analysis of the output features from each node was conducted. The results from experiments show that this method can accurately locate fault nodes in simulation scenarios with a remarkable accuracy. Additionally, the graph neural network based feature modeling allows for a qualitative examination of how faults spread across nodes, which provides valuable insights for analyzing fault nodes. |
| title | Dynamic Fault Characteristics Evaluation in Power Grid |
| topic | Machine Learning Computation and Language Signal Processing |
| url | https://arxiv.org/abs/2311.16522 |