Dynamic cache partitioning-based redundancy time optimization for smart substation inspection.
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| Title: | Dynamic cache partitioning-based redundancy time optimization for smart substation inspection. |
|---|---|
| Authors: | Xiaoxu, Wang1, Xiaofan, Song2 419493437@qq.com, Hongming, Shen1, Congyin, Wu1, Wenjie, Cui1, Jianbo, Yu3, Yongzhong, Zhou3 |
| Source: | Journal of Power Technologies. 2025, Vol. 105 Issue 4, p365-375. 11p. |
| Subjects: | Electric substations, Cache memory, Engineering inspection, Digital transformation, Electronic data processing, Electric power distribution grids |
| Abstract: | Smart substations are the key to the intelligent construction of the power grid. Efficient data processing and inspection optimization are of great significance for the safe and stable operation of the power grid. However, current data - processing in smar t substations suffers from problems such as the mismatch between processing and loading time and long redundant time, which restricts its in-depth digital development. To address these issues, this paper first analyzes the relationship between the types an d capacities of data in smart substations and identifies the main causes of redundant data-processing time. Based on this, a cache partitioning strategy is formulated according to the data capacity characteristics, and the relationship among the inspecti on sequence, cache space, and redundant time is further explored. Combining the importance of equipment and historical failure rates, a screening scheme for the inspection sequence is established to reduce data-processing redundancy and optimize the inspec tion duration. Finally, taking the data capacity of a 500 kV smart substation as an example, the feasibility of this scheme is verified. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Power Technologies is the property of Warsaw University of Technology, Institute of Heat Engineering 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.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191829549 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dynamic cache partitioning-based redundancy time optimization for smart substation inspection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiaoxu%2C+Wang%22">Xiaoxu, Wang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xiaofan%2C+Song%22">Xiaofan, Song</searchLink><relatesTo>2</relatesTo><i> 419493437@qq.com</i><br /><searchLink fieldCode="AR" term="%22Hongming%2C+Shen%22">Hongming, Shen</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Congyin%2C+Wu%22">Congyin, Wu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wenjie%2C+Cui%22">Wenjie, Cui</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Jianbo%2C+Yu%22">Jianbo, Yu</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Yongzhong%2C+Zhou%22">Yongzhong, Zhou</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Power+Technologies%22">Journal of Power Technologies</searchLink>. 2025, Vol. 105 Issue 4, p365-375. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electric+substations%22">Electric substations</searchLink><br /><searchLink fieldCode="DE" term="%22Cache+memory%22">Cache memory</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+inspection%22">Engineering inspection</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+transformation%22">Digital transformation</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+distribution+grids%22">Electric power distribution grids</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Smart substations are the key to the intelligent construction of the power grid. Efficient data processing and inspection optimization are of great significance for the safe and stable operation of the power grid. However, current data - processing in smar t substations suffers from problems such as the mismatch between processing and loading time and long redundant time, which restricts its in-depth digital development. To address these issues, this paper first analyzes the relationship between the types an d capacities of data in smart substations and identifies the main causes of redundant data-processing time. Based on this, a cache partitioning strategy is formulated according to the data capacity characteristics, and the relationship among the inspecti on sequence, cache space, and redundant time is further explored. Combining the importance of equipment and historical failure rates, a screening scheme for the inspection sequence is established to reduce data-processing redundancy and optimize the inspec tion duration. Finally, taking the data capacity of a 500 kV smart substation as an example, the feasibility of this scheme is verified. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Power Technologies is the property of Warsaw University of Technology, Institute of Heat Engineering 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 365 Subjects: – SubjectFull: Electric substations Type: general – SubjectFull: Cache memory Type: general – SubjectFull: Engineering inspection Type: general – SubjectFull: Digital transformation Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Electric power distribution grids Type: general Titles: – TitleFull: Dynamic cache partitioning-based redundancy time optimization for smart substation inspection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiaoxu, Wang – PersonEntity: Name: NameFull: Xiaofan, Song – PersonEntity: Name: NameFull: Hongming, Shen – PersonEntity: Name: NameFull: Congyin, Wu – PersonEntity: Name: NameFull: Wenjie, Cui – PersonEntity: Name: NameFull: Jianbo, Yu – PersonEntity: Name: NameFull: Yongzhong, Zhou IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20834187 Numbering: – Type: volume Value: 105 – Type: issue Value: 4 Titles: – TitleFull: Journal of Power Technologies Type: main |
| ResultId | 1 |
