Fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph.
The smart grid is on the basis of physical grid, introducing all kinds of advanced communications technology and form a new type of power grid. It can not only meet the demand of users and realize the optimal allocation of resources, but also improve the safety, economy and reliability of power supp...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315143 |
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| author | Lin Jun Zhou Chenliang |
| author_facet | Lin Jun Zhou Chenliang |
| author_sort | Lin Jun |
| collection | DOAJ |
| description | The smart grid is on the basis of physical grid, introducing all kinds of advanced communications technology and form a new type of power grid. It can not only meet the demand of users and realize the optimal allocation of resources, but also improve the safety, economy and reliability of power supply, it has become a major trend in the future development of electric power industry. But on the other hand, the complex network architecture of smart grid and the application of various high-tech technologies have also greatly increased the probability of equipment failure and the difficulty of fault diagnosis, and timely discovery and diagnosis of problems in the operation of smart grid equipment has become a key measure to ensure the safety of power grid operation. From the current point of view, the existing smart grid equipment fault diagnosis technology has problems that the application program is more complex, and the fault diagnosis rate is generally not high, which greatly affects the efficiency of smart grid maintenance. Therefore, Based on this, this paper adopts the multimodal semantic model of deep learning and knowledge graph, and on the basis of the original target detection network YOLOv4 architecture, introduces knowledge graph to unify the characterization and storage of the input multimodal information, and innovatively combines the YOLOv4 target detection algorithm with the knowledge graph to establish a smart grid equipment fault diagnosis model. Experiments show that compared with the existing fault detection algorithms, the YOLOv4 algorithm constructed in this paper is more accurate, faster and easier to operate. |
| format | Article |
| id | doaj-art-fb20136ed2934b4faea477d4a5456539 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-fb20136ed2934b4faea477d4a54565392025-08-20T03:09:35ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031514310.1371/journal.pone.0315143Fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph.Lin JunZhou ChenliangThe smart grid is on the basis of physical grid, introducing all kinds of advanced communications technology and form a new type of power grid. It can not only meet the demand of users and realize the optimal allocation of resources, but also improve the safety, economy and reliability of power supply, it has become a major trend in the future development of electric power industry. But on the other hand, the complex network architecture of smart grid and the application of various high-tech technologies have also greatly increased the probability of equipment failure and the difficulty of fault diagnosis, and timely discovery and diagnosis of problems in the operation of smart grid equipment has become a key measure to ensure the safety of power grid operation. From the current point of view, the existing smart grid equipment fault diagnosis technology has problems that the application program is more complex, and the fault diagnosis rate is generally not high, which greatly affects the efficiency of smart grid maintenance. Therefore, Based on this, this paper adopts the multimodal semantic model of deep learning and knowledge graph, and on the basis of the original target detection network YOLOv4 architecture, introduces knowledge graph to unify the characterization and storage of the input multimodal information, and innovatively combines the YOLOv4 target detection algorithm with the knowledge graph to establish a smart grid equipment fault diagnosis model. Experiments show that compared with the existing fault detection algorithms, the YOLOv4 algorithm constructed in this paper is more accurate, faster and easier to operate.https://doi.org/10.1371/journal.pone.0315143 |
| spellingShingle | Lin Jun Zhou Chenliang Fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph. PLoS ONE |
| title | Fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph. |
| title_full | Fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph. |
| title_fullStr | Fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph. |
| title_full_unstemmed | Fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph. |
| title_short | Fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph. |
| title_sort | fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph |
| url | https://doi.org/10.1371/journal.pone.0315143 |
| work_keys_str_mv | AT linjun fastfaultdiagnosisofsmartgridequipmentbasedondeepneuralnetworkmodelbasedonknowledgegraph AT zhouchenliang fastfaultdiagnosisofsmartgridequipmentbasedondeepneuralnetworkmodelbasedonknowledgegraph |