Estimating Leakage Inductance in High-Frequency Transformers Using an Artificial Neural Network and a Gray Wolf Optimizer-Based Hybrid Algorithm.
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| Title: | Estimating Leakage Inductance in High-Frequency Transformers Using an Artificial Neural Network and a Gray Wolf Optimizer-Based Hybrid Algorithm. |
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| Authors: | Kul, Seda1 (AUTHOR) sbalci@kmu.edu.tr, Yapıcı, Hamza2 (AUTHOR), Balci, Selami1,3 (AUTHOR), Shahnia, Farhad1,3 (AUTHOR) f.shahnia@murdoch.edu.au |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 12, p2905. 16p. |
| Subject Terms: | *Artificial neural networks, *Grey Wolf Optimizer algorithm, *Electric transformers, *Finite element method, *Algorithms, *Electric inductance, *Power electronics |
| Abstract: | The trend in the power electronics industry toward higher power density and efficiency has brought high-frequency transformers (HFTs) to the forefront of critical applications, including isolated DC–DC converters, electric vehicle chargers, and solid-state transformers. This paper focuses on the leakage inductance of HFTs and presents a systematic comparative framework that evaluates five surrogate modeling and hybrid optimization approaches for the rapid and accurate estimation of leakage inductance. A comprehensive parametric dataset was constructed, comprising 1210 finite element analysis simulations conducted via finite element analysis in the ANSYS Maxwell 2024 R1 environment, varying the number of winding turns, primary winding thickness, and secondary winding thickness of the HFT. All five methods were trained and evaluated on the same dataset under identical conditions. The comparative evaluation demonstrates that the proposed hybrid Gray Wolf optimizer–artificial neural network (GWO-ANN) framework achieved the highest prediction accuracy (R2 = 0.9832, MSE = 0.01780, MAE = 0.0935 µH) and the fastest convergence among all tested approaches. The generalization capability of the proposed model was confirmed through blind validation tests across six geometric configurations spanning the full range of the design space, yielding a maximum prediction error of 8.15% and an average error of 2.14%. The functional validity of the proposed parameters was further tested in a third validation layer using MATLAB/Simulink R2024b transformer circuit studies, demonstrating a theoretical efficiency of 96.06%. This three-layer validation approach proves both the parametric and functional reliability of the proposed framework for HFT designs. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194909354 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Estimating Leakage Inductance in High-Frequency Transformers Using an Artificial Neural Network and a Gray Wolf Optimizer-Based Hybrid Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kul%2C+Seda%22">Kul, Seda</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sbalci@kmu.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Yapıcı%2C+Hamza%22">Yapıcı, Hamza</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Balci%2C+Selami%22">Balci, Selami</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shahnia%2C+Farhad%22">Shahnia, Farhad</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> f.shahnia@murdoch.edu.au</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2905. 16p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+transformers%22">Electric transformers</searchLink><br />*<searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+inductance%22">Electric inductance</searchLink><br />*<searchLink fieldCode="DE" term="%22Power+electronics%22">Power electronics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The trend in the power electronics industry toward higher power density and efficiency has brought high-frequency transformers (HFTs) to the forefront of critical applications, including isolated DC–DC converters, electric vehicle chargers, and solid-state transformers. This paper focuses on the leakage inductance of HFTs and presents a systematic comparative framework that evaluates five surrogate modeling and hybrid optimization approaches for the rapid and accurate estimation of leakage inductance. A comprehensive parametric dataset was constructed, comprising 1210 finite element analysis simulations conducted via finite element analysis in the ANSYS Maxwell 2024 R1 environment, varying the number of winding turns, primary winding thickness, and secondary winding thickness of the HFT. All five methods were trained and evaluated on the same dataset under identical conditions. The comparative evaluation demonstrates that the proposed hybrid Gray Wolf optimizer–artificial neural network (GWO-ANN) framework achieved the highest prediction accuracy (R2 = 0.9832, MSE = 0.01780, MAE = 0.0935 µH) and the fastest convergence among all tested approaches. The generalization capability of the proposed model was confirmed through blind validation tests across six geometric configurations spanning the full range of the design space, yielding a maximum prediction error of 8.15% and an average error of 2.14%. The functional validity of the proposed parameters was further tested in a third validation layer using MATLAB/Simulink R2024b transformer circuit studies, demonstrating a theoretical efficiency of 96.06%. This three-layer validation approach proves both the parametric and functional reliability of the proposed framework for HFT designs. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194909354 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19122905 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 2905 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Grey Wolf Optimizer algorithm Type: general – SubjectFull: Electric transformers Type: general – SubjectFull: Finite element method Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Electric inductance Type: general – SubjectFull: Power electronics Type: general Titles: – TitleFull: Estimating Leakage Inductance in High-Frequency Transformers Using an Artificial Neural Network and a Gray Wolf Optimizer-Based Hybrid Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kul, Seda – PersonEntity: Name: NameFull: Yapıcı, Hamza – PersonEntity: Name: NameFull: Balci, Selami – PersonEntity: Name: NameFull: Shahnia, Farhad 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 |
