Chaotic Optimization of BP Neural Networks for Oil-Paper Insulated Transformer Life Prediction Based on Health Index Models.
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| Title: | Chaotic Optimization of BP Neural Networks for Oil-Paper Insulated Transformer Life Prediction Based on Health Index Models. |
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| Authors: | Wang, Minhao1 (AUTHOR), Song, Bin1 (AUTHOR) binsong@whu.edu.cn |
| Source: | Energies (19961073). Mar2026, Vol. 19 Issue 6, p1469. 12p. |
| Subject Terms: | *Remaining useful life, *Power transformers, *Weibull distribution, *Failure time data analysis, *Mathematical optimization, *Prediction models, *Multilayer perceptrons |
| Abstract: | The aging of oil-paper insulated transformer components significantly impacts their service life. Accurate health assessment is crucial for predicting failure rates and residual life, which is vital for ensuring operational safety. This paper employs the bathtub curve concept and Weibull distribution to fit collected oil-paper insulated transformer failure rate data, obtaining the failure rate curve. Considering operational environment and load factors, a health index model is established for residual life prediction. By optimizing the weight and bias parameters of the backpropagation (BP) neural network using an adaptive chaotic sequence strategy, a multi-parameter correlated transformer life prediction model is constructed. A cross-validation mechanism is introduced to enhance the model's generalization ability. Experimental results from training and testing demonstrate that the proposed method achieves higher prediction accuracy, with average errors of 5.36% for annual failure rate and 3.32% for residual life, confirming its effectiveness and applicability in transformer life prediction. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 192592643 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Chaotic Optimization of BP Neural Networks for Oil-Paper Insulated Transformer Life Prediction Based on Health Index Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Minhao%22">Wang, Minhao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Bin%22">Song, Bin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> binsong@whu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Mar2026, Vol. 19 Issue 6, p1469. 12p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Remaining+useful+life%22">Remaining useful life</searchLink><br />*<searchLink fieldCode="DE" term="%22Power+transformers%22">Power transformers</searchLink><br />*<searchLink fieldCode="DE" term="%22Weibull+distribution%22">Weibull distribution</searchLink><br />*<searchLink fieldCode="DE" term="%22Failure+time+data+analysis%22">Failure time data analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br />*<searchLink fieldCode="DE" term="%22Multilayer+perceptrons%22">Multilayer perceptrons</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The aging of oil-paper insulated transformer components significantly impacts their service life. Accurate health assessment is crucial for predicting failure rates and residual life, which is vital for ensuring operational safety. This paper employs the bathtub curve concept and Weibull distribution to fit collected oil-paper insulated transformer failure rate data, obtaining the failure rate curve. Considering operational environment and load factors, a health index model is established for residual life prediction. By optimizing the weight and bias parameters of the backpropagation (BP) neural network using an adaptive chaotic sequence strategy, a multi-parameter correlated transformer life prediction model is constructed. A cross-validation mechanism is introduced to enhance the model's generalization ability. Experimental results from training and testing demonstrate that the proposed method achieves higher prediction accuracy, with average errors of 5.36% for annual failure rate and 3.32% for residual life, confirming its effectiveness and applicability in transformer life prediction. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192592643 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19061469 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1469 Subjects: – SubjectFull: Remaining useful life Type: general – SubjectFull: Power transformers Type: general – SubjectFull: Weibull distribution Type: general – SubjectFull: Failure time data analysis Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Multilayer perceptrons Type: general Titles: – TitleFull: Chaotic Optimization of BP Neural Networks for Oil-Paper Insulated Transformer Life Prediction Based on Health Index Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Minhao – PersonEntity: Name: NameFull: Song, Bin IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 6 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
