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Volume 14
Issue 3
10.3390/electronics14030524
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Dynamic to Static Model Comparison and Hybrid Metaheuristic Optimization in Induction Motor Parameter Estimation

by
Nelson H. B. Santana
1,
Imene Yahyaoui
2,*,
Flavio D. C. Oliveira
3,
Arthur E. A. Amorim
1,
Domingos S. L. Simonetti
4 and
Helder R. O. Rocha
4
1
Coordination of Electrical Engineering, Federal Institute of Espírito Santo, Rod. BR-101, km 58, São Mateus 29932-540, ES, Brazil
2
Department Applied Mathematics, Materials Science and Engineering and Electronics Technology, University Rey Juan Carlos, 28922 Alcorcón, Spain
3
Department of Computation and Electronics, Federal University of Espírito Santo, Rod. BR-101, km 58, São Mateus 29932-540, ES, Brazil
4
Department of Electrical Engineering, Federal University of Espírito Santo, Av. Fernando Ferrari, 514, Vitória 29075-910, ES, Brazil
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(3), 524; https://doi.org/10.3390/electronics14030524
Submission received: 16 November 2024 / Revised: 21 January 2025 / Accepted: 22 January 2025 / Published: 28 January 2025
(This article belongs to the Special Issue Section Collection Series: New Horizons and Recent Advances in Power Electronics)
Versions Notes

Abstract

This paper presents a comprehensive study of parameter estimation for three-phase induction motors (IMs) using hybrid optimization methods and a comparative evaluation of static and dynamic modeling approaches. A hybrid metaheuristic combining the Sine Cosine Algorithm (SCA) and Particle Swarm Optimization (PSO) is developed to identify optimal motor parameters efficiently. The approach utilizes a static model for rapid estimation, with final parameter values validated against a dynamic model to ensure accuracy in operational predictions. Results confirm that the static model provides robust parameter estimates for key performance metrics, including torque, power factor, and current, aligning well with experimental results from real-motor no-load tests. Parameters estimated by the proposed method demonstrate a high adherence with the motor real measurements. Comparisons also reveal the limitations of static models in scenarios requiring state-space accuracy, such as observer-based control applications. This study concludes by recommending further exploration of alternative motor modeling structures and the hybrid optimization algorithm for parameter estimation.
Keywords: three-phase induction motor; static model; dynamic model; sine cosine algorithm; particle swarm optimization; metaheuristic-based optimization three-phase induction motor; static model; dynamic model; sine cosine algorithm; particle swarm optimization; metaheuristic-based optimization

Share and Cite

MDPI and ACS Style

Santana, N.H.B.; Yahyaoui, I.; Oliveira, F.D.C.; Amorim, A.E.A.; Simonetti, D.S.L.; Rocha, H.R.O. Dynamic to Static Model Comparison and Hybrid Metaheuristic Optimization in Induction Motor Parameter Estimation. Electronics 2025, 14, 524. https://doi.org/10.3390/electronics14030524

AMA Style

Santana NHB, Yahyaoui I, Oliveira FDC, Amorim AEA, Simonetti DSL, Rocha HRO. Dynamic to Static Model Comparison and Hybrid Metaheuristic Optimization in Induction Motor Parameter Estimation. Electronics. 2025; 14(3):524. https://doi.org/10.3390/electronics14030524

Chicago/Turabian Style

Santana, Nelson H. B., Imene Yahyaoui, Flavio D. C. Oliveira, Arthur E. A. Amorim, Domingos S. L. Simonetti, and Helder R. O. Rocha. 2025. "Dynamic to Static Model Comparison and Hybrid Metaheuristic Optimization in Induction Motor Parameter Estimation" Electronics 14, no. 3: 524. https://doi.org/10.3390/electronics14030524

APA Style

Santana, N. H. B., Yahyaoui, I., Oliveira, F. D. C., Amorim, A. E. A., Simonetti, D. S. L., & Rocha, H. R. O. (2025). Dynamic to Static Model Comparison and Hybrid Metaheuristic Optimization in Induction Motor Parameter Estimation. Electronics, 14(3), 524. https://doi.org/10.3390/electronics14030524

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