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

Fausto Valencia, Hugo Arcos and Franklin Quilumba
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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

Energies, 2021, vol. 14, issue 14, 1-27

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.

Keywords: artificial neural networks; deep learning; power transformers; stress; finite element method; electromagnetic forces (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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