Academic Journal
Fault diagnosis of intelligent substation relay protection system based on transformer architecture and migration training model.
| Τίτλος: | Fault diagnosis of intelligent substation relay protection system based on transformer architecture and migration training model. |
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| Συγγραφείς: | Mei, Yao, Ni, Saisai, Zhang, Haibo |
| Πηγή: | Energy Informatics; 11/19/2024, Vol. 7 Issue 1, p1-18, 18p |
| Θεματικοί όροι: | Pattern recognition systems, Fault diagnosis, Transformer models, Technology transfer, Sustainable development |
| Περίληψη: | In the context of global energy transformation, the construction of smart grids is becoming a novel vogue in the evolution of power systems. As the core node of the smart grid, the efficient operation of the intelligent substation relay protection system is essential to the safety and stability of the power system. However, with the expansion of power grid-scale and complexity, traditional relay protection systems need help with fault diagnosis accuracy and response speed. This study proposes a fault diagnosis scheme of an intelligent substation relay protection system based on Transformer architecture and migration training model, aiming at improving the intelligent level of fault diagnosis. By introducing the Transformer architecture, the model can efficiently process high-dimensional and nonlinear complex data of substations, significantly improving the accuracy of fault pattern recognition from 82% of the original model to 96%, and the response speed is also increased by 30%. At the same time, using transfer learning technology, the adaptability and generalization capabilities of the model in new scenarios have been significantly enhanced, reducing the dependence on a large amount of new data and accelerating the deployment of the model among different substations. The experimental results show that this scheme can quickly and accurately identify various fault types and effectively locate fault points. This study not only promotes the development of intelligent technology for power systems but also lays a solid foundation for the safe and stable operation of smart grids and the sustainable development of the power industry. [ABSTRACT FROM AUTHOR] |
| Copyright of Energy Informatics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1186/s42162-024-00429-w Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edb DbLabel: Complementary Index An: 180990651 RelevancyScore: 974 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 973.596313476563 |
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| Items | – Name: Title Label: Title Group: Ti Data: Fault diagnosis of intelligent substation relay protection system based on transformer architecture and migration training model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mei%2C+Yao%22">Mei, Yao</searchLink><br /><searchLink fieldCode="AR" term="%22Ni%2C+Saisai%22">Ni, Saisai</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Haibo%22">Zhang, Haibo</searchLink> – Name: TitleSource Label: Source Group: Src Data: Energy Informatics; 11/19/2024, Vol. 7 Issue 1, p1-18, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+transfer%22">Technology transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainable+development%22">Sustainable development</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the context of global energy transformation, the construction of smart grids is becoming a novel vogue in the evolution of power systems. As the core node of the smart grid, the efficient operation of the intelligent substation relay protection system is essential to the safety and stability of the power system. However, with the expansion of power grid-scale and complexity, traditional relay protection systems need help with fault diagnosis accuracy and response speed. This study proposes a fault diagnosis scheme of an intelligent substation relay protection system based on Transformer architecture and migration training model, aiming at improving the intelligent level of fault diagnosis. By introducing the Transformer architecture, the model can efficiently process high-dimensional and nonlinear complex data of substations, significantly improving the accuracy of fault pattern recognition from 82% of the original model to 96%, and the response speed is also increased by 30%. At the same time, using transfer learning technology, the adaptability and generalization capabilities of the model in new scenarios have been significantly enhanced, reducing the dependence on a large amount of new data and accelerating the deployment of the model among different substations. The experimental results show that this scheme can quickly and accurately identify various fault types and effectively locate fault points. This study not only promotes the development of intelligent technology for power systems but also lays a solid foundation for the safe and stable operation of smart grids and the sustainable development of the power industry. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Energy Informatics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s42162-024-00429-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Pattern recognition systems Type: general – SubjectFull: Fault diagnosis Type: general – SubjectFull: Transformer models Type: general – SubjectFull: Technology transfer Type: general – SubjectFull: Sustainable development Type: general Titles: – TitleFull: Fault diagnosis of intelligent substation relay protection system based on transformer architecture and migration training model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mei, Yao – PersonEntity: Name: NameFull: Ni, Saisai – PersonEntity: Name: NameFull: Zhang, Haibo IsPartOfRelationships: – BibEntity: Dates: – D: 19 M: 11 Text: 11/19/2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 25208942 Numbering: – Type: volume Value: 7 – Type: issue Value: 1 Titles: – TitleFull: Energy Informatics Type: main |
| ResultId | 1 |