Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model Article Swipe
Meng-Yun Lee
,
Yu-Shan Huang
,
Chia-Jui Chang
,
Jiayu Yang
,
Chih‐Wen Liu
,
Tzu-Chiao Lin
,
Yen-Bor Lin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14104031
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14104031
Ensuring a consistently reliable power supply is paramount in power systems. Researchers are engaged in the pursuit of categorizing transmission line failures to design countermeasures for mitigating the associated financial losses. Our study employs a machine learning-based methodology, specifically the Conformer Convolution-Augmented Transformer model, to classify transmission line fault types. This model processes time series input data directly, eliminating the need for expert feature extraction. The training and validation datasets are generated through simulations conducted on a two-terminal transmission line, while testing is conducted on historical data consisting of 108 events that occurred in the Taiwan power system. Due to the limited availability of historical data, they are utilized solely for inference purposes. Our simulations are meticulously designed to encompass potential faults based on an analysis of historical data. A significant aspect of our investigation focuses on the impact of the sampling rate on input data, establishing that a rate of four samples per cycle is sufficient. This suggests that, for our specific classification tasks, relying on lower frequency data might be adequate, thereby challenging the conventional emphasis on high-frequency analysis. Eventually, our methodology achieves a validation accuracy of 100%, although the testing accuracy is lower at 88.88%. The discrepancy in testing accuracy can be attributed to the limited information and the small number of historical events, which pose challenges in bridging the gap between simulated data and real-world measurements. Furthermore, we benchmarked our method against the ELM model proposed in 2023, demonstrating significant improvements in testing accuracy.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app14104031
- https://www.mdpi.com/2076-3417/14/10/4031/pdf?version=1715261759
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4396760451Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app14104031Digital Object Identifier
- Title
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Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-09Full publication date if available
- Authors
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Meng-Yun Lee, Yu-Shan Huang, Chia-Jui Chang, Jiayu Yang, Chih‐Wen Liu, Tzu-Chiao Lin, Yen-Bor LinList of authors in order
- Landing page
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https://doi.org/10.3390/app14104031Publisher landing page
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https://www.mdpi.com/2076-3417/14/10/4031/pdf?version=1715261759Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/14/10/4031/pdf?version=1715261759Direct OA link when available
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Transmission line, Computer science, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5102964924 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I16733864 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
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| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.73174183 |
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| citation_normalized_percentile.is_in_top_10_percent | False |