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Math. Comput. Appl. 1996, 1(2), 87-92; doi:10.3390/mca1020087

Minimum Permeance Estimation of Variable Reluctance Machines by Using Neural Networks

Rensselaer Polytechnic Institute, Department of Electric Power Engineering, Troy, NY, USA
Published: 1 December 1996
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Abstract

A new approach was studied in this paper to calculate minimum permeance (Pmin) of variable reluctance machines (VRM). Finite element method (FEM) and neural network (NN) were employed together for estimation. The data collected by an electromagnetic finite element software (Flux 2D) were used to train NN. Trained NN was tested by another data set which are not in the training data set. Total estimation error in the test set was observed less than 2.5%. A similar study was performed with the data set collected using flux tube analysis (FTA). In this case, much larger data set was constructed by FTA since this method allows to generate larger data set. After training NN by this data set, it was tested by a test set generated by FTA. The total estimation error was observed less than 5%.
Keywords: Variable Reluctance Machine; Artificial Neural Network; Finite elementmethod; Minimum Permeance Variable Reluctance Machine; Artificial Neural Network; Finite elementmethod; Minimum Permeance
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Mese, E. Minimum Permeance Estimation of Variable Reluctance Machines by Using Neural Networks. Math. Comput. Appl. 1996, 1, 87-92.

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Math. Comput. Appl. EISSN 2297-8747 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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