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Establishment and Optimization of Stator Bar End Model Based on SHO-RBF.

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Title: Establishment and Optimization of Stator Bar End Model Based on SHO-RBF.
Authors: Liu, Yanli1 (AUTHOR), Gao, Junguo1 (AUTHOR) gaojunguo@hrbust.edu.cn, Hu, Haitao1 (AUTHOR), Lang, Peiye1 (AUTHOR)
Source: Energies (19961073). Mar2026, Vol. 19 Issue 6, p1476. 17p.
Subject Terms: *Mathematical optimization, *Radial basis functions, *Metaheuristic algorithms, *Simulation methods & models, *Artificial neural networks, *Electric field strength, *Corona discharge
Abstract: To establish the complex functional relationship between the stator bar end structure and the maximum electric field strength, and to optimize the anti-corona structure, an optimization model for the stator bar end based on the Seahorse Optimization algorithm—Radial Basis Function (SHO-RBF) neural network is proposed in this paper. The RBF neural network is employed to establish the complex relationship between the maximum electric field strength at the stator bar end and the anti-corona structure parameters. The SHO is introduced to find the optimal anti-corona structure at the stator bar end structure. A simulation model of the stator bar end is developed, and 30 sets of simulation data are collected for training and optimization purposes. The relationship between the stator bar end structure and the maximum electric field strength is established, and an optimized scheme comprising six groups of anti-corona structures is developed. The feasibility of the proposed design is validated through simulation calculations. Compared to manually adjusting parameters individually within the simulation model, this approach offers a significant advantage in terms of computational efficiency and speed. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 192592650
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Establishment and Optimization of Stator Bar End Model Based on SHO-RBF.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Yanli%22">Liu, Yanli</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Junguo%22">Gao, Junguo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gaojunguo@hrbust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Haitao%22">Hu, Haitao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lang%2C+Peiye%22">Lang, Peiye</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Mar2026, Vol. 19 Issue 6, p1476. 17p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Radial+basis+functions%22">Radial basis functions</searchLink><br />*<searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+field+strength%22">Electric field strength</searchLink><br />*<searchLink fieldCode="DE" term="%22Corona+discharge%22">Corona discharge</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To establish the complex functional relationship between the stator bar end structure and the maximum electric field strength, and to optimize the anti-corona structure, an optimization model for the stator bar end based on the Seahorse Optimization algorithm—Radial Basis Function (SHO-RBF) neural network is proposed in this paper. The RBF neural network is employed to establish the complex relationship between the maximum electric field strength at the stator bar end and the anti-corona structure parameters. The SHO is introduced to find the optimal anti-corona structure at the stator bar end structure. A simulation model of the stator bar end is developed, and 30 sets of simulation data are collected for training and optimization purposes. The relationship between the stator bar end structure and the maximum electric field strength is established, and an optimized scheme comprising six groups of anti-corona structures is developed. The feasibility of the proposed design is validated through simulation calculations. Compared to manually adjusting parameters individually within the simulation model, this approach offers a significant advantage in terms of computational efficiency and speed. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192592650
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19061476
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 1476
    Subjects:
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Radial basis functions
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Simulation methods & models
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Electric field strength
        Type: general
      – SubjectFull: Corona discharge
        Type: general
    Titles:
      – TitleFull: Establishment and Optimization of Stator Bar End Model Based on SHO-RBF.
        Type: main
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          Name:
            NameFull: Liu, Yanli
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            NameFull: Gao, Junguo
      – PersonEntity:
          Name:
            NameFull: Hu, Haitao
      – PersonEntity:
          Name:
            NameFull: Lang, Peiye
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          Dates:
            – D: 15
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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              Value: 19
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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