| Issue |
E3S Web Conf.
Volume 705, 2026
Advances in Renewable Energy & Electric Vehicles (AREEV-2026) (under the aegis of ICETE 2026 Multi-Conference Platform)
|
|
|---|---|---|
| Article Number | 02002 | |
| Number of page(s) | 11 | |
| Section | Control Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202670502002 | |
| Published online | 15 April 2026 | |
Offline Parameter Estimation of an Induction Motor Based on a Complete Mathematical Model
1 Department of Electrical and Electronics, NMAM Institute of Technology, Nitte, India
2 Hexmoto Controls Private Limited, Mysore, India
Abstract
This paper presents an offline parameter estimation method for a mathematical model of an induction motor. This technique would enable algorithm fine-tuning in real-world industrial applications. A DC injection and low-frequency AC injection technique are used to estimate the motor parameters. The former technique is used to estimate the stator resistance while the latter to estimate rotor resistance, leakage inductances and magnetizing inductance. The mathematical model of the test induction motor is validated in multiple reference frames including the stator, rotor and synchronous. Simulations are performed in MATLAB-Simulink to validate the estimation techniques and determine their accuracy. The obtained results are promising with an accuracy of 95%. This research provides a foundation for the development and performance evaluation of vector control algorithms for induction motors.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.