Main attributes.
<div><p>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 relia...
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2025
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| _version_ | 1852022697408593920 |
|---|---|
| author | Lin Jun (2187955) |
| author2 | Zhou Chenliang (20727158) |
| author2_role | author |
| author_facet | Lin Jun (2187955) Zhou Chenliang (20727158) |
| author_role | author |
| dc.creator.none.fl_str_mv | Lin Jun (2187955) Zhou Chenliang (20727158) |
| dc.date.none.fl_str_mv | 2025-02-14T18:28:16Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0315143.t003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Main_attributes_/28419521 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Developmental Biology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified yolov4 algorithm constructed multimodal semantic model input multimodal information advanced communications technology electric power industry smart grid maintenance introduces knowledge graph complex network architecture also greatly increased fault diagnosis rate fast fault diagnosis smart grid equipment power grid operation smart grid power grid knowledge graph fault diagnosis power supply physical grid greatly affects equipment failure also improve xlink "> timely discovery tech technologies optimal allocation new type major trend key measure innovatively combines future development experiments show deep learning current point |
| dc.title.none.fl_str_mv | Main attributes. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>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.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_0130f1c09e43bb3ca45b0c2ac5efbd96 |
| identifier_str_mv | 10.1371/journal.pone.0315143.t003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28419521 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Main attributes.Lin Jun (2187955)Zhou Chenliang (20727158)Developmental BiologyCancerScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedyolov4 algorithm constructedmultimodal semantic modelinput multimodal informationadvanced communications technologyelectric power industrysmart grid maintenanceintroduces knowledge graphcomplex network architecturealso greatly increasedfault diagnosis ratefast fault diagnosissmart grid equipmentpower grid operationsmart gridpower gridknowledge graphfault diagnosispower supplyphysical gridgreatly affectsequipment failurealso improvexlink ">timely discoverytech technologiesoptimal allocationnew typemajor trendkey measureinnovatively combinesfuture developmentexperiments showdeep learningcurrent point<div><p>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.</p></div>2025-02-14T18:28:16ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0315143.t003https://figshare.com/articles/dataset/Main_attributes_/28419521CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284195212025-02-14T18:28:16Z |
| spellingShingle | Main attributes. Lin Jun (2187955) Developmental Biology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified yolov4 algorithm constructed multimodal semantic model input multimodal information advanced communications technology electric power industry smart grid maintenance introduces knowledge graph complex network architecture also greatly increased fault diagnosis rate fast fault diagnosis smart grid equipment power grid operation smart grid power grid knowledge graph fault diagnosis power supply physical grid greatly affects equipment failure also improve xlink "> timely discovery tech technologies optimal allocation new type major trend key measure innovatively combines future development experiments show deep learning current point |
| status_str | publishedVersion |
| title | Main attributes. |
| title_full | Main attributes. |
| title_fullStr | Main attributes. |
| title_full_unstemmed | Main attributes. |
| title_short | Main attributes. |
| title_sort | Main attributes. |
| topic | Developmental Biology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified yolov4 algorithm constructed multimodal semantic model input multimodal information advanced communications technology electric power industry smart grid maintenance introduces knowledge graph complex network architecture also greatly increased fault diagnosis rate fast fault diagnosis smart grid equipment power grid operation smart grid power grid knowledge graph fault diagnosis power supply physical grid greatly affects equipment failure also improve xlink "> timely discovery tech technologies optimal allocation new type major trend key measure innovatively combines future development experiments show deep learning current point |