Professor MICHELE DEGANO Michele.Degano@nottingham.ac.uk
PROFESSOR OF ADVANCED ELECTRICAL MACHINES
HF induction motor modeling using Genetic Algorithms and experimental impedance measurement
Degano, Marco; Zanchetta, Pericle; Clare, Jon; Empringham, Lee
Authors
Pericle Zanchetta
Jon Clare
Professor LEE EMPRINGHAM LEE.EMPRINGHAM@NOTTINGHAM.AC.UK
PROFESSOR OF POWER CONVERSION TECHNOLOGIES
Abstract
High frequency (HF) models of electrical motors and converters are of basic importance for Electro Magnetic Compatibility (EMC) characterization of electrical drives and for EMI filters design. In this paper a method for characterizing and tuning HF models (150kHz - 30MHz) for induction motors has been proposed based on experimental measurements of the motor impedance. Impedance measurements have been taken in different configurations: 1) between all the winding terminals shorted and ground; 2) between the input terminals shorted and ground; 3) between one input terminal and the other two shorted. Once a HF motor model structure has been chosen and modeled using a state space representation, its parameters have been tuned using GA algorithm to match the real impedance in each corresponding configuration. Comparison between experimental impedance measurements and model estimation are shown for all tested configurations to validate the model within the frequency range of interest for EMI. © 2010 IEEE.
Citation
Degano, M., Zanchetta, P., Clare, J., & Empringham, L. (2010, July). HF induction motor modeling using Genetic Algorithms and experimental impedance measurement. Presented at 2010 IEEE International Symposium on Industrial Electronics, Bari, Italy
Conference Name | 2010 IEEE International Symposium on Industrial Electronics |
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Start Date | Jul 4, 2010 |
End Date | Jul 7, 2010 |
Online Publication Date | Nov 15, 2010 |
Publication Date | Dec 27, 2010 |
Deposit Date | Jun 24, 2024 |
Pages | 1296-1301 |
ISBN | 9781424463916 |
DOI | https://doi.org/10.1109/ISIE.2010.5637052 |
Public URL | https://nottingham-repository.worktribe.com/output/35446464 |
Publisher URL | https://ieeexplore.ieee.org/document/5637052 |
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