The paper deals with the flux linkages estimation of a synchronous motor during speed or load transients. The proposed method is based on the radial basis function neural network, which returns continuous functions between currents and magnetic flux linkages, considering all the nonlinearities. The training of the neural network weights is realised by an innovative training algorithm able to cope with both steady state and variable working conditions, conversely to most of the identification techniques. The method was validated throughout several tests performed on a synchronous reluctance motor electric drive.

Magnetic model identification for synchronous reluctance motors including transients

Ortombina L.
;
Pasqualotto D.;Tinazzi F.;Zigliotto M.
2019

Abstract

The paper deals with the flux linkages estimation of a synchronous motor during speed or load transients. The proposed method is based on the radial basis function neural network, which returns continuous functions between currents and magnetic flux linkages, considering all the nonlinearities. The training of the neural network weights is realised by an innovative training algorithm able to cope with both steady state and variable working conditions, conversely to most of the identification techniques. The method was validated throughout several tests performed on a synchronous reluctance motor electric drive.
2019
2019 IEEE Energy Conversion Congress and Exposition, ECCE 2019
11th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2019
978-1-7281-0395-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3325870
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