Intelligent information systems for power grid fault analysis by computer communication technology
Abstract This study aims to enhance the intelligence level of power grid fault analysis to address increasingly complex fault scenarios and ensure grid stability and security. To this end, an intelligent information system for power grid fault analysis, leveraging improved computer communication tec...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-024-00465-6 |
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| _version_ | 1832594440619819008 |
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| author | Ronglong Xu Jing Zhang |
| author_facet | Ronglong Xu Jing Zhang |
| author_sort | Ronglong Xu |
| collection | DOAJ |
| description | Abstract This study aims to enhance the intelligence level of power grid fault analysis to address increasingly complex fault scenarios and ensure grid stability and security. To this end, an intelligent information system for power grid fault analysis, leveraging improved computer communication technology, is proposed and developed. The system incorporates a novel fault diagnosis model, combining advanced communication technologies such as distributed computing, real-time data transmission, cloud computing, and big data analytics, to establish a multi-layered information processing architecture for grid fault analysis. Specifically, this study introduces a fusion model integrating Transformer self-attention mechanisms with graph neural networks (GNNs) based on conventional fault diagnosis techniques. GNNs capture the complex relationships between different nodes within the grid topology, effectively identifying power transmission characteristics and fault propagation paths across grid nodes. The Transformer’s self-attention mechanism processes time-series operational data from the grid, enabling precise identification of temporal dependencies in fault characteristics. To improve system response speed, edge computing moves portions of fault data preprocessing and analysis to edge nodes near data sources, significantly reducing transmission latency and enhancing real-time diagnosis capability. Experimental results demonstrate that the proposed model achieves superior diagnostic performance across various fault types (e.g., short circuits, overloads, equipment failures) in simulation scenarios. The system achieves a fault identification and location accuracy of 99.2%, an improvement of over 10% compared to traditional methods, with an average response time of 85 milliseconds, approximately 43% faster than existing technologies. Moreover, the system exhibits strong robustness in complex scenarios, with an average fault prediction error rate of just 1.1% across multiple simulations. This study provides a novel solution for intelligent power grid fault diagnosis and management, establishing a technological foundation for smart grid operations. |
| format | Article |
| id | doaj-art-cbc30d06025d435d9a13eecf9e7092da |
| institution | Kabale University |
| issn | 2520-8942 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-cbc30d06025d435d9a13eecf9e7092da2025-01-19T12:40:36ZengSpringerOpenEnergy Informatics2520-89422025-01-018112010.1186/s42162-024-00465-6Intelligent information systems for power grid fault analysis by computer communication technologyRonglong Xu0Jing Zhang1Computer Engineering School, Weifang UniversitySports School, Weifang UniversityAbstract This study aims to enhance the intelligence level of power grid fault analysis to address increasingly complex fault scenarios and ensure grid stability and security. To this end, an intelligent information system for power grid fault analysis, leveraging improved computer communication technology, is proposed and developed. The system incorporates a novel fault diagnosis model, combining advanced communication technologies such as distributed computing, real-time data transmission, cloud computing, and big data analytics, to establish a multi-layered information processing architecture for grid fault analysis. Specifically, this study introduces a fusion model integrating Transformer self-attention mechanisms with graph neural networks (GNNs) based on conventional fault diagnosis techniques. GNNs capture the complex relationships between different nodes within the grid topology, effectively identifying power transmission characteristics and fault propagation paths across grid nodes. The Transformer’s self-attention mechanism processes time-series operational data from the grid, enabling precise identification of temporal dependencies in fault characteristics. To improve system response speed, edge computing moves portions of fault data preprocessing and analysis to edge nodes near data sources, significantly reducing transmission latency and enhancing real-time diagnosis capability. Experimental results demonstrate that the proposed model achieves superior diagnostic performance across various fault types (e.g., short circuits, overloads, equipment failures) in simulation scenarios. The system achieves a fault identification and location accuracy of 99.2%, an improvement of over 10% compared to traditional methods, with an average response time of 85 milliseconds, approximately 43% faster than existing technologies. Moreover, the system exhibits strong robustness in complex scenarios, with an average fault prediction error rate of just 1.1% across multiple simulations. This study provides a novel solution for intelligent power grid fault diagnosis and management, establishing a technological foundation for smart grid operations.https://doi.org/10.1186/s42162-024-00465-6Power Grid Fault AnalysisGraph neural networksSelf-attention mechanismEdge ComputingIntelligent Information System |
| spellingShingle | Ronglong Xu Jing Zhang Intelligent information systems for power grid fault analysis by computer communication technology Energy Informatics Power Grid Fault Analysis Graph neural networks Self-attention mechanism Edge Computing Intelligent Information System |
| title | Intelligent information systems for power grid fault analysis by computer communication technology |
| title_full | Intelligent information systems for power grid fault analysis by computer communication technology |
| title_fullStr | Intelligent information systems for power grid fault analysis by computer communication technology |
| title_full_unstemmed | Intelligent information systems for power grid fault analysis by computer communication technology |
| title_short | Intelligent information systems for power grid fault analysis by computer communication technology |
| title_sort | intelligent information systems for power grid fault analysis by computer communication technology |
| topic | Power Grid Fault Analysis Graph neural networks Self-attention mechanism Edge Computing Intelligent Information System |
| url | https://doi.org/10.1186/s42162-024-00465-6 |
| work_keys_str_mv | AT ronglongxu intelligentinformationsystemsforpowergridfaultanalysisbycomputercommunicationtechnology AT jingzhang intelligentinformationsystemsforpowergridfaultanalysisbycomputercommunicationtechnology |