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http://ciateq.repositorioinstitucional.mx/jspui/handle/1020/430| Hot-spot temperature forecasting of the instrument transformer using an artificial neural network | |
| EDGAR ALFREDO JUÁREZ BALDERAS Joselito Medina-Marin Juan C. Olivares-Galvan Norberto Hernández Romero Juan Carlos Seck Tuoh Mora Alejandro Rodriguez-Aguilar | |
| Acceso Abierto | |
| Atribución-NoComercial-CompartirIgual | |
| 2169-3536 10.1109/ACCESS.2020.3021673 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/document/9186082 | |
| Artificial neural networks Resin-cast instrument transformer Epoxy resins Finite element analysis | |
| Cast resin medium voltage instrument transformer are highly used because of several benefits over other type of transformers. Nevertheless, the high operating temperatures affects their performance and durability. It is important to forecast the hot spots in the transformer. The aim of this study is to develop a model based on Artificial Neural Networks (ANN) theory to be able to forecast the temperature in seven points, taking into account twenty-six input data of transformer design features. 792 simulations were carried out in COMSOL Multiphysics® to emulate the heat transfer in the transformer. The data obtained were used to train 1110 ANN with different number of neurons and hidden layers. The ANN with the best performance (R D 1, MSE D 0.003455) has three hidden layers with 10, 9 and 9 neurons respectively. The ANN predictions were validated with finite element simulations and laboratory thermal tests which present similar patterns. With this accuracy in the prediction of hot-spot temperature, this ANN can be used to optimize the design of instrument transformers. This work was supported in part by the Consejo Nacional de Ciencia y Tecnología (CONACYT) in coordination with the Postgrado de CIATEQ, A. C., Mexico; in part by the Universidad Autónoma del Estado de Hidalgo under Project CONACYT CB-2017-2018-A1-S-43008; in part by the Universidad Autónoma Metropolitana; and in part by the company Arteche North America, S. A. de C. V. | |
| IEEE | |
| 2020 | |
| Artículo | |
| IEEE Access, v. 8, p. 164392-164406 | |
| Inglés | |
| Público en general | |
| Juarez-Balderas, E. A., Medina-Marin, J., Olivares-Galvan, J. C., Hernandez-Romero, N., Seck-Tuoh-Mora, J. C., & Rodriguez-Aguilar, A. (2020). Hot-spot temperature forecasting of the instrument transformer using an artificial neural network. IEEE Access, 8, 164392-164406. https://doi.org/10.1109/ACCESS.2020.3021673 | |
| INTELIGENCIA ARTIFICIAL | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos Publicados en Revistas Indexadas |
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| Hot-spot temperature forecasting.pdf | 3.3 MB | Adobe PDF | Visualizar/Abrir |