Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches
A key component for the performance, availability, and reliability of power grids is the power transformer. Although power transformers are very reliable assets, the early detection of incipient degradation mechanisms is very important to preventing failures that may shorten their residual life. In...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/1/77 |
| _version_ | 1797358870873505792 |
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| author | Guillermo Santamaria-Bonfil Gustavo Arroyo-Figueroa Miguel A. Zuniga-Garcia Carlos Gustavo Azcarraga Ramos Ali Bassam |
| author_facet | Guillermo Santamaria-Bonfil Gustavo Arroyo-Figueroa Miguel A. Zuniga-Garcia Carlos Gustavo Azcarraga Ramos Ali Bassam |
| author_sort | Guillermo Santamaria-Bonfil |
| collection | DOAJ |
| description | A key component for the performance, availability, and reliability of power grids is the power transformer. Although power transformers are very reliable assets, the early detection of incipient degradation mechanisms is very important to preventing failures that may shorten their residual life. In this work, a comparative analysis of standard machine learning (ML) algorithms (such as single and ensemble classification algorithms) and automatic machine learning (autoML) classifiers is presented for the fault diagnosis of power transformers. The goal of this research is to determine whether fully automated ML approaches are better or worse than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from dissolved gas analysis results. The methodology uses a transformer fault database (TDB) gathered from specialized databases and technical literature. Fault data were processed using the Duval pentagon diagnosis approach and user–expert knowledge. Parameters from both single and ensemble classifiers were optimized through standard machine learning procedures. The results showed that the best-suited algorithm to tackle the problem is a robust, automatic machine learning classifier model, followed by standard algorithms, such as neural networks and stacking ensembles. These results highlight the ability of a robust, automatic machine learning model to handle unbalanced power transformer fault datasets with high accuracy, requiring minimum tuning effort by electrical experts. We also emphasize that identifying the most probable transformer fault condition will reduce the time required to find and solve a fault. |
| first_indexed | 2024-03-08T15:08:29Z |
| format | Article |
| id | doaj.art-78dded2281ef47508fe8b90d9370cd48 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T15:08:29Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-78dded2281ef47508fe8b90d9370cd482024-01-10T14:55:42ZengMDPI AGEnergies1996-10732023-12-011717710.3390/en17010077Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML ApproachesGuillermo Santamaria-Bonfil0Gustavo Arroyo-Figueroa1Miguel A. Zuniga-Garcia2Carlos Gustavo Azcarraga Ramos3Ali Bassam4Data Portfolio Manager Department, BBVA Mexico, Mexico City 06600, MexicoInstituto Nacional de Electricidad y Energias Limpias, Cuernavaca 62490, MexicoPCI Energy Solutions, Norman, OK 73072, USAInstituto Nacional de Electricidad y Energias Limpias, Cuernavaca 62490, MexicoFacultad de Ingeniería, Universidad Autónoma de Yucatán, Merida 97000, MexicoA key component for the performance, availability, and reliability of power grids is the power transformer. Although power transformers are very reliable assets, the early detection of incipient degradation mechanisms is very important to preventing failures that may shorten their residual life. In this work, a comparative analysis of standard machine learning (ML) algorithms (such as single and ensemble classification algorithms) and automatic machine learning (autoML) classifiers is presented for the fault diagnosis of power transformers. The goal of this research is to determine whether fully automated ML approaches are better or worse than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from dissolved gas analysis results. The methodology uses a transformer fault database (TDB) gathered from specialized databases and technical literature. Fault data were processed using the Duval pentagon diagnosis approach and user–expert knowledge. Parameters from both single and ensemble classifiers were optimized through standard machine learning procedures. The results showed that the best-suited algorithm to tackle the problem is a robust, automatic machine learning classifier model, followed by standard algorithms, such as neural networks and stacking ensembles. These results highlight the ability of a robust, automatic machine learning model to handle unbalanced power transformer fault datasets with high accuracy, requiring minimum tuning effort by electrical experts. We also emphasize that identifying the most probable transformer fault condition will reduce the time required to find and solve a fault.https://www.mdpi.com/1996-1073/17/1/77transformer fault diagnosismachine learningautomatic machine learningpower systems |
| spellingShingle | Guillermo Santamaria-Bonfil Gustavo Arroyo-Figueroa Miguel A. Zuniga-Garcia Carlos Gustavo Azcarraga Ramos Ali Bassam Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches Energies transformer fault diagnosis machine learning automatic machine learning power systems |
| title | Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches |
| title_full | Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches |
| title_fullStr | Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches |
| title_full_unstemmed | Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches |
| title_short | Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches |
| title_sort | power transformer fault detection a comparison of standard machine learning and automl approaches |
| topic | transformer fault diagnosis machine learning automatic machine learning power systems |
| url | https://www.mdpi.com/1996-1073/17/1/77 |
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