A Comprehensive Review of Transformer Winding Diagnostics: Integrating Frequency Response Analysis with Machine Learning Approaches
Frequency Response Analysis (FRA) is a proven method for detecting mechanical faults in transformers, such as winding deformations and short circuits. However, traditional FRA interpretation relies heavily on visual and subjective comparison of frequency response curves, which can introduce human bi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/5/1209 |
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| Summary: | Frequency Response Analysis (FRA) is a proven method for detecting mechanical faults in transformers, such as winding deformations and short circuits. However, traditional FRA interpretation relies heavily on visual and subjective comparison of frequency response curves, which can introduce human bias and lead to inconsistent results. Integrating Machine Learning (ML) with FRA can significantly enhance fault diagnosis by automatically identifying complex patterns within the data that are difficult to detect using through human analysis. This integration can automate diagnostics, enhance accuracy, improve predictive maintenance, reduce reliance on expert interpretation and curtail operational costs. This paper reviews the application of FRA and ML alongside complementary techniques for transformer winding health assessment. |
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| ISSN: | 1996-1073 |