View in EDS HTML Full Text PDF Full Text

A Hybrid GB-PINN Framework for Efficient Prediction of Arc Parameters in Low-Voltage Electrical Contacts.

Saved in:
Bibliographic Details
Title: A Hybrid GB-PINN Framework for Efficient Prediction of Arc Parameters in Low-Voltage Electrical Contacts.
Authors: Li, Wenhua1 (AUTHOR), Wang, Zishuai1,2 (AUTHOR), Pan, Chao2,3 (AUTHOR) chaopan2025@163.com, Zhao, Qian3,4 (AUTHOR), Meng, Xianchun1 (AUTHOR), Liu, Chao2,4 (AUTHOR), Xu, Zilin1,3 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 12, p2823. 20p.
Subject Terms: *Magnetohydrodynamics, *Gaussian mixture models, *Electric contacts, *Mathematical optimization, *Data augmentation
Abstract: Low-voltage electrical contacts are core components of power distribution systems, renewable energy installations, and industrial automation equipment. The electric arc generated during contact switching is the primary cause of contact erosion, material transfer, and equipment failure, posing significant threats to system reliability and operational safety. The accurate prediction of arc parameters is hindered by two challenges: the high scatter in available data undermines empirical models, and purely data-driven approaches risk physically implausible results. To address this, a Gaussian Mixture-enhanced Bayesian-optimized Physics-Informed Neural Network (GB-PINN) is proposed. Three core contributions are made: (1) High-fidelity MHD simulation foundation: A magnetohydrodynamic (MHD) multi-physics coupling model of the contact arc was constructed and validated against experiments, showing high fidelity with only 1.63% error in arc duration and 1.82% in arc energy. A multivariate simulation dataset was generated by varying key contact parameters based on this validated model. (2) GMM-based data augmentation: The measured and simulated data were modeled and sampled via Gaussian Mixture Model (GMM) to enrich the dataset while preserving physical consistency. (3) BOHB-optimized PINN prediction: The Bayesian Optimization and Hyperband (BOHB) algorithm was employed to optimize the PINN hyperparameters, enhancing training efficiency and predictive accuracy. Experimental results demonstrated that the proposed GB-PINN achieved superior performance in predicting arc duration and energy, with mean absolute errors (MAE) of 0.079 ms and 0.624 mJ, root mean square errors (RMSE) of 0.099 ms and 0.774 mJ, and coefficients of determination (R2) of 0.980 and 0.979, significantly outperforming grey model (GM (1, N)), long short-term memory (LSTM), and Transformer models. As a physics-informed data-driven tool, GB-PINN enables high-precision arc prediction, providing reliable support for electrical contact design. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 194909272
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Hybrid GB-PINN Framework for Efficient Prediction of Arc Parameters in Low-Voltage Electrical Contacts.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li%2C+Wenhua%22">Li, Wenhua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Zishuai%22">Wang, Zishuai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Chao%22">Pan, Chao</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> chaopan2025@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Qian%22">Zhao, Qian</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Meng%2C+Xianchun%22">Meng, Xianchun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Chao%22">Liu, Chao</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Zilin%22">Xu, Zilin</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2823. 20p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Magnetohydrodynamics%22">Magnetohydrodynamics</searchLink><br />*<searchLink fieldCode="DE" term="%22Gaussian+mixture+models%22">Gaussian mixture models</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+contacts%22">Electric contacts</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Low-voltage electrical contacts are core components of power distribution systems, renewable energy installations, and industrial automation equipment. The electric arc generated during contact switching is the primary cause of contact erosion, material transfer, and equipment failure, posing significant threats to system reliability and operational safety. The accurate prediction of arc parameters is hindered by two challenges: the high scatter in available data undermines empirical models, and purely data-driven approaches risk physically implausible results. To address this, a Gaussian Mixture-enhanced Bayesian-optimized Physics-Informed Neural Network (GB-PINN) is proposed. Three core contributions are made: (1) High-fidelity MHD simulation foundation: A magnetohydrodynamic (MHD) multi-physics coupling model of the contact arc was constructed and validated against experiments, showing high fidelity with only 1.63% error in arc duration and 1.82% in arc energy. A multivariate simulation dataset was generated by varying key contact parameters based on this validated model. (2) GMM-based data augmentation: The measured and simulated data were modeled and sampled via Gaussian Mixture Model (GMM) to enrich the dataset while preserving physical consistency. (3) BOHB-optimized PINN prediction: The Bayesian Optimization and Hyperband (BOHB) algorithm was employed to optimize the PINN hyperparameters, enhancing training efficiency and predictive accuracy. Experimental results demonstrated that the proposed GB-PINN achieved superior performance in predicting arc duration and energy, with mean absolute errors (MAE) of 0.079 ms and 0.624 mJ, root mean square errors (RMSE) of 0.099 ms and 0.774 mJ, and coefficients of determination (R2) of 0.980 and 0.979, significantly outperforming grey model (GM (1, N)), long short-term memory (LSTM), and Transformer models. As a physics-informed data-driven tool, GB-PINN enables high-precision arc prediction, providing reliable support for electrical contact design. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194909272
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19122823
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 2823
    Subjects:
      – SubjectFull: Magnetohydrodynamics
        Type: general
      – SubjectFull: Gaussian mixture models
        Type: general
      – SubjectFull: Electric contacts
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Data augmentation
        Type: general
    Titles:
      – TitleFull: A Hybrid GB-PINN Framework for Efficient Prediction of Arc Parameters in Low-Voltage Electrical Contacts.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Li, Wenhua
      – PersonEntity:
          Name:
            NameFull: Wang, Zishuai
      – PersonEntity:
          Name:
            NameFull: Pan, Chao
      – PersonEntity:
          Name:
            NameFull: Zhao, Qian
      – PersonEntity:
          Name:
            NameFull: Meng, Xianchun
      – PersonEntity:
          Name:
            NameFull: Liu, Chao
      – PersonEntity:
          Name:
            NameFull: Xu, Zilin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 12
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
ResultId 1