Power Transformer Fault Prediction Using Dissolved Gas Analysis and Neural Networks.
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| Title: | Power Transformer Fault Prediction Using Dissolved Gas Analysis and Neural Networks. |
|---|---|
| Authors: | Bessa, Alcebíades Rangel1 (AUTHOR), Fardin, Jussara Farias1 (AUTHOR), Ciarelli, Patrick Marques1 (AUTHOR), Encarnação, Lucas Frizera1 (AUTHOR) lucas.encarnacao@ufes.br |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 12, p2934. 26p. |
| Subject Terms: | *Power transformers, *Artificial neural networks, *Recurrent neural networks, *Long short-term memory, *Failure analysis, *Equipment maintenance & repair, *Multilayer perceptrons, *Methane |
| Abstract: | In this work, we present a neural network-based study capable of predicting faults in oil-insulated power transformers through the analysis of dissolved gases. The advantage of this study lies in using data already collected by electric power companies, which gather it to comply with international or regional standards; however, they sometimes act only after the equipment is already in a faulty condition. Therefore, the challenge in this work was data regularization, as collections typically occur at long intervals of 6 to 12 months. Furthermore, samples are often irregular, as data collection depends on factors such as weather and the availability of maintenance teams. As a result of this work, Multilayer Perceptron (MLP), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) were used to predict failures with advanced forecasts ranging from 1 to 6 months, achieving accuracies of 97.5% and 85%, respectively. Thus, these models prove to be important tools for maintenance planning, enabling adequate predictability for organizing equipment shutdowns without the need for high investments in installing tools to capture this information online and adapting substations to send data to control rooms or other analysis centers. [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: 194909383 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Power Transformer Fault Prediction Using Dissolved Gas Analysis and Neural Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bessa%2C+Alcebíades+Rangel%22">Bessa, Alcebíades Rangel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fardin%2C+Jussara+Farias%22">Fardin, Jussara Farias</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ciarelli%2C+Patrick+Marques%22">Ciarelli, Patrick Marques</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Encarnação%2C+Lucas+Frizera%22">Encarnação, Lucas Frizera</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lucas.encarnacao@ufes.br</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2934. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Power+transformers%22">Power transformers</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br />*<searchLink fieldCode="DE" term="%22Failure+analysis%22">Failure analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Equipment+maintenance+%26+repair%22">Equipment maintenance & repair</searchLink><br />*<searchLink fieldCode="DE" term="%22Multilayer+perceptrons%22">Multilayer perceptrons</searchLink><br />*<searchLink fieldCode="DE" term="%22Methane%22">Methane</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this work, we present a neural network-based study capable of predicting faults in oil-insulated power transformers through the analysis of dissolved gases. The advantage of this study lies in using data already collected by electric power companies, which gather it to comply with international or regional standards; however, they sometimes act only after the equipment is already in a faulty condition. Therefore, the challenge in this work was data regularization, as collections typically occur at long intervals of 6 to 12 months. Furthermore, samples are often irregular, as data collection depends on factors such as weather and the availability of maintenance teams. As a result of this work, Multilayer Perceptron (MLP), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) were used to predict failures with advanced forecasts ranging from 1 to 6 months, achieving accuracies of 97.5% and 85%, respectively. Thus, these models prove to be important tools for maintenance planning, enabling adequate predictability for organizing equipment shutdowns without the need for high investments in installing tools to capture this information online and adapting substations to send data to control rooms or other analysis centers. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194909383 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19122934 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 2934 Subjects: – SubjectFull: Power transformers Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Recurrent neural networks Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Failure analysis Type: general – SubjectFull: Equipment maintenance & repair Type: general – SubjectFull: Multilayer perceptrons Type: general – SubjectFull: Methane Type: general Titles: – TitleFull: Power Transformer Fault Prediction Using Dissolved Gas Analysis and Neural Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bessa, Alcebíades Rangel – PersonEntity: Name: NameFull: Fardin, Jussara Farias – PersonEntity: Name: NameFull: Ciarelli, Patrick Marques – PersonEntity: Name: NameFull: Encarnação, Lucas Frizera 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 |
