Measurement error evaluation method for voltage transformers in distribution networks based on self-attention and graph convolutional networks Article Swipe
Xiaoyang Zeng
,
Tong Liu
,
Huiqin Xie
,
Dajiang Wang
,
Jun Xiao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s42162-025-00525-5
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s42162-025-00525-5
Accurately evaluating the error of voltage transformers in distribution networks is crucial for the safe operation of power systems and the fairness of electricity trade. This paper uses the connection relationship between distribution transformers and voltage transformers to predict the secondary voltage of voltage transformers through the secondary voltage of transformers, constructing a voltage transfer characteristic model between the two to achieve accurate evaluation of voltage transformer errors. To address the challenge of extracting complex nonlinear features from multivariate electrical data, a combined model of a self-attention mechanism and a graph convolutional network (GCN) is proposed. The self-attention mechanism captures global dependencies among power parameters, while the GCN effectively constructs the multivariate data structures in distribution networks. By integrating both approaches, the model can fully extract the intrinsic features of the data as well as the hidden dependency information between data points. Additionally, to prevent gradient vanishing as the combined model’s structure deepens, a multi-head residual structure is introduced to enhance the self-attention mechanism. Experimental results show that compared to a single model, the proposed combined model reduces the mean squared error by 82.35% and increases the coefficient of determination R2 by 9.07%, demonstrating significant accuracy advantages in voltage transformer error evaluation.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s42162-025-00525-5
- https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00525-5
- OA Status
- gold
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410517949
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- OpenAlex ID
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https://openalex.org/W4410517949Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s42162-025-00525-5Digital Object Identifier
- Title
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Measurement error evaluation method for voltage transformers in distribution networks based on self-attention and graph convolutional networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-20Full publication date if available
- Authors
-
Xiaoyang Zeng, Tong Liu, Huiqin Xie, Dajiang Wang, Jun XiaoList of authors in order
- Landing page
-
https://doi.org/10.1186/s42162-025-00525-5Publisher landing page
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https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00525-5Direct 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://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00525-5Direct OA link when available
- Concepts
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Computer science, Graph, Voltage, Transformer, Reliability engineering, Algorithm, Electronic engineering, Theoretical computer science, Electrical engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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23Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.electrical | 80 |
| abstract_inverted_index.evaluating | 2 |
| abstract_inverted_index.evaluation | 64 |
| abstract_inverted_index.extracting | 74 |
| abstract_inverted_index.introduced | 159 |
| abstract_inverted_index.mechanism. | 164 |
| abstract_inverted_index.multi-head | 155 |
| abstract_inverted_index.structures | 114 |
| abstract_inverted_index.approaches, | 121 |
| abstract_inverted_index.coefficient | 188 |
| abstract_inverted_index.effectively | 109 |
| abstract_inverted_index.electricity | 24 |
| abstract_inverted_index.evaluation. | 202 |
| abstract_inverted_index.information | 139 |
| abstract_inverted_index.integrating | 119 |
| abstract_inverted_index.parameters, | 105 |
| abstract_inverted_index.significant | 195 |
| abstract_inverted_index.transformer | 67, 200 |
| abstract_inverted_index.Experimental | 165 |
| abstract_inverted_index.constructing | 52 |
| abstract_inverted_index.dependencies | 102 |
| abstract_inverted_index.distribution | 9, 33, 116 |
| abstract_inverted_index.multivariate | 79, 112 |
| abstract_inverted_index.relationship | 31 |
| abstract_inverted_index.transformers | 7, 34, 37, 45 |
| abstract_inverted_index.Additionally, | 143 |
| abstract_inverted_index.convolutional | 92 |
| abstract_inverted_index.demonstrating | 194 |
| abstract_inverted_index.determination | 190 |
| abstract_inverted_index.transformers, | 51 |
| abstract_inverted_index.characteristic | 56 |
| abstract_inverted_index.self-attention | 87, 98, 163 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.23859569 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |