A New Neural Network based Method for Online Parameters Identification of the Interior Permanent Magnet Synchronous Machines

This paper presents a new method to identity online four parameters of the interior permanent magnet synchronous motors (IPMSM), including stator resistance, d-axis inductance, q-axis inductance and permanent magnet flux linkage. The proposed method is based on the neural network with the training d...

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Bibliographic Details
Published inIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society pp. 1 - 6
Main Authors Bui, Minh Xuan, Minh Pham, Viet
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.10.2022
Subjects
Couplings
Inductance
IPMSM
Kalman Filter
Neural Network
Online Parameter Identification
Parameter estimation
Recursive Least Square
Resistance
Rotors
Torque
Training data
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Summary:This paper presents a new method to identity online four parameters of the interior permanent magnet synchronous motors (IPMSM), including stator resistance, d-axis inductance, q-axis inductance and permanent magnet flux linkage. The proposed method is based on the neural network with the training data taken from experiments, which were preprocessed before feeding to the input of the neural network model. The proposed online parameters estimation method is evaluated by comparing the estimation accuracy with other conventional online methods, such as Extended Kalman Filter, Recursive Least Square and the Adaline Neural Network. Extensive numerical simulations have been conducted to verify the effectiveness and the accuracy of the proposed method.
ISSN:2577-1647
DOI:10.1109/IECON49645.2022.9969069