Publication: Prediction of power losses in transformer cores using feed forward neural network and genetic algorithm
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Küçük, İlker
Derebaşı, Naim
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Elsevier
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Abstract
A mathematical model for core losses was improved for frequency and geometrical effects using experimental data obtained from toroidal wound cores. The improved mathematical model was applied to the other soft magnetic materials and optimizes its parameters with the aim of neural networks. A 6-neuron input layer, 9-neuron output layer model with two hidden layers were developed. While the input neurons were geometrical parameters, magnetising frequency, magnetic induction and resistivity of the soft magnetic materials, output neurons were correlation coefficients and the power loss. The network has been trained by the genetic algorithm. The linear correlation coefficient was found to be 99%.
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Keywords
Engineering, Instruments & instrumentation, Toroidal magnetic cores, Power loss, Genetic algorithm, Artificial neural network, Optimization, Neural networks, Mathematical models, Magnetization, Genetic algorithms, Computational geometry, Toroidal magnetic cores, Power loss, Geometrical effects, Electric transformers, Frequency, Magnetic-properties, Toroidal cores
Citation
Küçük, İ. ve Derebaşı, N. (2006). ''Prediction of power losses in transformer cores using feed forward neural network and genetic algorithm''. Measurement: Journal of the International Measurement Confederation, 39(7), 605-611.