A synergetic neural network-genetic scheme for optimal transformer construction.
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| Title: | A synergetic neural network-genetic scheme for optimal transformer construction. |
|---|---|
| Authors: | Doulamis, Nikolaos D., Doulamis, Anastasios D., Georgilakis, Pavlos S., Kollias, Stefanos D., Hatziargyriou, Nikos D. |
| Source: | Integrated Computer-Aided Engineering. 2002, Vol. 9 Issue 1, p37. 20p. |
| Subjects: | Artificial neural networks, Genetic algorithms |
| Abstract: | In this paper, a combined neural network and an evolutionary programming scheme is proposed to improve the quality of wound core distribution transformers in an industrial environment by exploiting information derived from both the construction and transformer design phase. In particular, the neural network architecture is responsible for predicting transformer iron losses prior to their assembly, based on several actual core measurements, transformer design parameters and the specific core assembling. A genetic algorithm is applied to estimate the optimal core arrangement, (i.e. the way of core assembling) that yields a set of three-phase transformers of minimal iron losses. The minimization is performed by exploiting information derived from the neural network model resulting in a synergetic neural network-genetic algorithm scheme. After the transformer construction, the prediction accuracy of the neural network model is evaluated. If accuracy is poor, a weight adaptation algorithm is applied to improve the prediction performance. For the weight updating, both the current and the previous network knowledge are taken into account. Application of the proposed neural network-genetic algorithm scheme to our industrial environment indicates a significant reduction in the variation between the actual and the designed transformer iron losses. This further leads to a reduction of the production cost since a smaller safety margin can be used for the transformer design. [ABSTRACT FROM AUTHOR] |
| Copyright of Integrated Computer-Aided Engineering is the property of Sage Publications Inc. 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: 5846224 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A synergetic neural network-genetic scheme for optimal transformer construction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Doulamis%2C+Nikolaos+D%2E%22">Doulamis, Nikolaos D.</searchLink><br /><searchLink fieldCode="AR" term="%22Doulamis%2C+Anastasios+D%2E%22">Doulamis, Anastasios D.</searchLink><br /><searchLink fieldCode="AR" term="%22Georgilakis%2C+Pavlos+S%2E%22">Georgilakis, Pavlos S.</searchLink><br /><searchLink fieldCode="AR" term="%22Kollias%2C+Stefanos+D%2E%22">Kollias, Stefanos D.</searchLink><br /><searchLink fieldCode="AR" term="%22Hatziargyriou%2C+Nikos+D%2E%22">Hatziargyriou, Nikos D.</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Integrated+Computer-Aided+Engineering%22">Integrated Computer-Aided Engineering</searchLink>. 2002, Vol. 9 Issue 1, p37. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, a combined neural network and an evolutionary programming scheme is proposed to improve the quality of wound core distribution transformers in an industrial environment by exploiting information derived from both the construction and transformer design phase. In particular, the neural network architecture is responsible for predicting transformer iron losses prior to their assembly, based on several actual core measurements, transformer design parameters and the specific core assembling. A genetic algorithm is applied to estimate the optimal core arrangement, (i.e. the way of core assembling) that yields a set of three-phase transformers of minimal iron losses. The minimization is performed by exploiting information derived from the neural network model resulting in a synergetic neural network-genetic algorithm scheme. After the transformer construction, the prediction accuracy of the neural network model is evaluated. If accuracy is poor, a weight adaptation algorithm is applied to improve the prediction performance. For the weight updating, both the current and the previous network knowledge are taken into account. Application of the proposed neural network-genetic algorithm scheme to our industrial environment indicates a significant reduction in the variation between the actual and the designed transformer iron losses. This further leads to a reduction of the production cost since a smaller safety margin can be used for the transformer design. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Integrated Computer-Aided Engineering is the property of Sage Publications Inc. 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: Identifiers: – Type: doi Value: 10.3233/ICA-2002-9103 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 37 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Genetic algorithms Type: general Titles: – TitleFull: A synergetic neural network-genetic scheme for optimal transformer construction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Doulamis, Nikolaos D. – PersonEntity: Name: NameFull: Doulamis, Anastasios D. – PersonEntity: Name: NameFull: Georgilakis, Pavlos S. – PersonEntity: Name: NameFull: Kollias, Stefanos D. – PersonEntity: Name: NameFull: Hatziargyriou, Nikos D. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: 2002 Type: published Y: 2002 Identifiers: – Type: issn-print Value: 10692509 Numbering: – Type: volume Value: 9 – Type: issue Value: 1 Titles: – TitleFull: Integrated Computer-Aided Engineering Type: main |
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