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Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Author

Listed:
  • Fausto Valencia

    (Faculty of Electrical Engineering, Escuela Politecnica Nacional, Ladrón de Guevara 253, Quito 170517, Ecuador)

  • Hugo Arcos

    (Faculty of Electrical Engineering, Escuela Politecnica Nacional, Ladrón de Guevara 253, Quito 170517, Ecuador)

  • Franklin Quilumba

    (Faculty of Electrical Engineering, Escuela Politecnica Nacional, Ladrón de Guevara 253, Quito 170517, Ecuador)

Abstract

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

Suggested Citation

  • Fausto Valencia & Hugo Arcos & Franklin Quilumba, 2021. "Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis," Energies, MDPI, vol. 14(14), pages 1-27, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4242-:d:594023
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    Cited by:

    1. Wenbin Su & Wei Ren & Hui Sun & Canjie Liu & Xuhao Lu & Yingli Hua & Hongbo Wei & Han Jia, 2022. "Data-Based Flow Rate Prediction Models for Independent Metering Hydraulic Valve," Energies, MDPI, vol. 15(20), pages 1-12, October.

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