Multi-Source Information Fusion for Degradation Assessment of Metal-Oxide Surge Arresters in Power Systems.
Saved in:
| Title: | Multi-Source Information Fusion for Degradation Assessment of Metal-Oxide Surge Arresters in Power Systems. |
|---|---|
| Authors: | Zhong, Dantian1 (AUTHOR), Cai, Zhiyuan1 (AUTHOR) caizhiyuan111@126.com |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 10, p2450. 29p. |
| Subject Terms: | *Multisensor data fusion, *Convolutional neural networks, *Long short-term memory, *Electric power systems, *Optimization algorithms |
| Abstract: | As the scale of ultra-high-voltage (UHV) and extra-high-voltage (EHV) transmission networks continues to expand, the operational reliability of surge arresters has become increasingly important for power-system security. Based on equivalent degradation experiments conducted on a 1000 kV class UHV surge arrester, this study proposes a multi-source information fusion approach for degradation-state assessment. Leakage-current, UHF partial-discharge, voltage, and temperature-field data were jointly used to construct a hybrid framework integrating a multi-branch convolutional neural network (CNN) and a long short-term memory (LSTM) network. To improve model performance, the sparrow search algorithm (SSA) was introduced for hyperparameter optimization. Experimental results show that the proposed method achieved accuracies of 97.47% and 94.23% on the training and test sets, respectively, and was able to distinguish the normal condition from different degraded-section conditions under the laboratory-emulated equivalent degradation scenario considered in this study. These results indicate that multi-source information fusion combined with data-driven hyperparameter optimization is a feasible approach for laboratory-scale degradation assessment of surge arresters and provides a basis for further validation under more realistic service conditions. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: enr DbLabel: Energy & Power Source An: 194141564 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Multi-Source Information Fusion for Degradation Assessment of Metal-Oxide Surge Arresters in Power Systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhong%2C+Dantian%22">Zhong, Dantian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cai%2C+Zhiyuan%22">Cai, Zhiyuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> caizhiyuan111@126.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2450. 29p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br />*<searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As the scale of ultra-high-voltage (UHV) and extra-high-voltage (EHV) transmission networks continues to expand, the operational reliability of surge arresters has become increasingly important for power-system security. Based on equivalent degradation experiments conducted on a 1000 kV class UHV surge arrester, this study proposes a multi-source information fusion approach for degradation-state assessment. Leakage-current, UHF partial-discharge, voltage, and temperature-field data were jointly used to construct a hybrid framework integrating a multi-branch convolutional neural network (CNN) and a long short-term memory (LSTM) network. To improve model performance, the sparrow search algorithm (SSA) was introduced for hyperparameter optimization. Experimental results show that the proposed method achieved accuracies of 97.47% and 94.23% on the training and test sets, respectively, and was able to distinguish the normal condition from different degraded-section conditions under the laboratory-emulated equivalent degradation scenario considered in this study. These results indicate that multi-source information fusion combined with data-driven hyperparameter optimization is a feasible approach for laboratory-scale degradation assessment of surge arresters and provides a basis for further validation under more realistic service conditions. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141564 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19102450 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 2450 Subjects: – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Electric power systems Type: general – SubjectFull: Optimization algorithms Type: general Titles: – TitleFull: Multi-Source Information Fusion for Degradation Assessment of Metal-Oxide Surge Arresters in Power Systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhong, Dantian – PersonEntity: Name: NameFull: Cai, Zhiyuan IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 10 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
