Deep Learning Model for Magnetic Field Prediction of Transformers Under Overvoltage Conditions Article Swipe
Qingjun Peng
,
Hui Du
,
Zezhong Zheng
,
Haowei Zhu
,
Yuhang Fang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/elp2.70063
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/elp2.70063
The transformer is an important equipment in power systems. However, prolonged abnormal conditions can lead to significant damage of the transformer equipment. The current finite element analysis (FEA) method for calculating the internal physical field of transformers is time‐consuming, limiting its practicality for fast simulation. This paper focuses on predicting the internal magnetic fields of transformers under overvoltage conditions, which cause irregular changes in the transformer magnetic fields due to overvoltage. Simulation datasets of transformer magnetic field under overvoltage conditions were acquired via the COMSOL software. Subsequent analysis elucidated the influence of overvoltage parameters on the electrical characteristics of transformers. Furthermore, the dimensionality of input features relevant to magnetic field prediction was expanded. Convolutional neural network (CNN) model was then employed to forecast the internal magnetic fields of transformers under overvoltage conditions. Experimental results were compared with Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and deep neural network (DNN) models, demonstrating the efficiency of deep learning methods in predicting transformer magnetic fields under overvoltage conditions.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/elp2.70063
- OA Status
- gold
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413206558
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413206558Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/elp2.70063Digital Object Identifier
- Title
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Deep Learning Model for Magnetic Field Prediction of Transformers Under Overvoltage ConditionsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Qingjun Peng, Hui Du, Zezhong Zheng, Haowei Zhu, Yuhang FangList of authors in order
- Landing page
-
https://doi.org/10.1049/elp2.70063Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1049/elp2.70063Direct OA link when available
- Concepts
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Overvoltage, Transformer, Current transformer, Magnetic field, Artificial neural network, Finite element method, Engineering, Electrical engineering, Electronic engineering, Computer science, Voltage, Artificial intelligence, Physics, Structural engineering, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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29Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3112194498, https://openalex.org/W3022348214, https://openalex.org/W2512262549, https://openalex.org/W2354173401, https://openalex.org/W2981307453, https://openalex.org/W2003448487, https://openalex.org/W2990240688, https://openalex.org/W3134684220, https://openalex.org/W3178590964, https://openalex.org/W3128873360, https://openalex.org/W2979711203, https://openalex.org/W2999123549, https://openalex.org/W2921710869, https://openalex.org/W4291156125, https://openalex.org/W3188145644, https://openalex.org/W4324125107, https://openalex.org/W4400438785, https://openalex.org/W4388871793, https://openalex.org/W2911964244, https://openalex.org/W2919115771, https://openalex.org/W3124539583, https://openalex.org/W2605418509, https://openalex.org/W4381381689, https://openalex.org/W2587288333, https://openalex.org/W3011193865, https://openalex.org/W2604944342, https://openalex.org/W3182706339, https://openalex.org/W3208261418, https://openalex.org/W2048040918 |
| referenced_works_count | 29 |
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