PCA and Autoencoder-Based ANN Models for Transformer Fault Diagnosis Using Dissolved Gas Analysis: Comparative Insights and Challenges.
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| Title: | PCA and Autoencoder-Based ANN Models for Transformer Fault Diagnosis Using Dissolved Gas Analysis: Comparative Insights and Challenges. |
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| Authors: | Nkwambe, Mwamba S.1 (AUTHOR), Thango, Bonginkosi A.1 (AUTHOR) bonginkosit@uj.ac.za |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 12, p2806. 30p. |
| Subject Terms: | *Fault diagnosis, *Feature extraction, *Artificial neural networks, *Autoencoders, *Dimensional reduction algorithms, *Principal components analysis |
| Abstract: | Accurate fault diagnosis of power transformers using Dissolved Gas Analysis (DGA) depends on effective feature extraction to reduce redundancy and improve classification performance. This study compares linear and nonlinear feature extraction methods viz. Principal Component Analysis (PCA) and bottleneck Autoencoders (AE) to determine whether nonlinear representations provide diagnostic advantages for transformer fault classification. A dataset of 595 IEC 60599-labeled DGA samples covering six fault classes (PD, D1, D2, T1, T2, T3) was used. A 15-dimensional feature space was constructed from gas concentrations, total hydrocarbon content, and IEC-aligned gas ratios. PCA and AE were applied for dimensionality reduction across latent dimensions (k = 1–15), followed by an identical Artificial Neural Network (ANN) classifier. Performance was evaluated using test accuracy, cross-validation stability, and per-class F1-scores. The PCA+ANN model achieved a maximum accuracy of 68.9% at k = 11, outperforming AE+ANN, which achieved 66.4% at k = 4. PCA also demonstrated greater cross-validation stability (62 ± 3.5%) compared to AE (62 ± 6.6%). However, AE improved F1-scores for discharge faults (D1 and D2) by enhancing nonlinear separation of overlapping samples. PCA provides superior overall accuracy and stability for transformer fault diagnosis, while AE offers targeted advantages in distinguishing discharge-related faults. These findings establish a consistent benchmark for future studies and highlight the complementary roles of linear and nonlinear feature extraction in DGA-based diagnostic systems. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194909255 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: PCA and Autoencoder-Based ANN Models for Transformer Fault Diagnosis Using Dissolved Gas Analysis: Comparative Insights and Challenges. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nkwambe%2C+Mwamba+S%2E%22">Nkwambe, Mwamba S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Thango%2C+Bonginkosi+A%2E%22">Thango, Bonginkosi A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bonginkosit@uj.ac.za</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2806. 30p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br />*<searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Autoencoders%22">Autoencoders</searchLink><br />*<searchLink fieldCode="DE" term="%22Dimensional+reduction+algorithms%22">Dimensional reduction algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate fault diagnosis of power transformers using Dissolved Gas Analysis (DGA) depends on effective feature extraction to reduce redundancy and improve classification performance. This study compares linear and nonlinear feature extraction methods viz. Principal Component Analysis (PCA) and bottleneck Autoencoders (AE) to determine whether nonlinear representations provide diagnostic advantages for transformer fault classification. A dataset of 595 IEC 60599-labeled DGA samples covering six fault classes (PD, D1, D2, T1, T2, T3) was used. A 15-dimensional feature space was constructed from gas concentrations, total hydrocarbon content, and IEC-aligned gas ratios. PCA and AE were applied for dimensionality reduction across latent dimensions (k = 1–15), followed by an identical Artificial Neural Network (ANN) classifier. Performance was evaluated using test accuracy, cross-validation stability, and per-class F1-scores. The PCA+ANN model achieved a maximum accuracy of 68.9% at k = 11, outperforming AE+ANN, which achieved 66.4% at k = 4. PCA also demonstrated greater cross-validation stability (62 ± 3.5%) compared to AE (62 ± 6.6%). However, AE improved F1-scores for discharge faults (D1 and D2) by enhancing nonlinear separation of overlapping samples. PCA provides superior overall accuracy and stability for transformer fault diagnosis, while AE offers targeted advantages in distinguishing discharge-related faults. These findings establish a consistent benchmark for future studies and highlight the complementary roles of linear and nonlinear feature extraction in DGA-based diagnostic systems. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194909255 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19122806 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 2806 Subjects: – SubjectFull: Fault diagnosis Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Autoencoders Type: general – SubjectFull: Dimensional reduction algorithms Type: general – SubjectFull: Principal components analysis Type: general Titles: – TitleFull: PCA and Autoencoder-Based ANN Models for Transformer Fault Diagnosis Using Dissolved Gas Analysis: Comparative Insights and Challenges. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nkwambe, Mwamba S. – PersonEntity: Name: NameFull: Thango, Bonginkosi A. 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 |
