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Genetic Algorithm Approach for Gains Selection of Induction Machine Extended Speed Observer

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  • Daniel Wachowiak

    (Department of Electric Drives and Energy Conversion, Faculty of Electrical and Control Engineering, Gdańsk University of Technology, 80-233 Gdańsk, Poland)

Abstract

The subject of this paper is gains selection of an extended induction machine speed observer. A high number of gains makes manual gains selection difficult and due to nonlinear equations of the observer, well-known methods of gains selection for linear systems cannot be applied. A method based on genetic algorithms has been proposed instead. Such an approach requires multiple fitness function calls; therefore, using a quality index based on simulations makes gains selection a time-consuming process. To find a fitness function that evaluates, in a short time, quality indices based on poles placement have been proposed. As the observer is nonlinear, equations describing the observer dynamics have been linearized. The relationship between poles placement and real dynamic properties has been shown. A series of studies has been performed to investigate the influence of the operating point of the machine on the dynamics of the observer. It has been proven that rotor speed has a significant impact on the placement of the poles and the observer may lose stability after a rotation direction change. A method of gains modification to maintain symmetrical properties of the observer for both directions has been presented. Experimental studies of the observer during machine reverse in the open and closed-loop control system have been performed. The results show that the observer can be implemented in a sensorless drive, using the proposed gains selection method.

Suggested Citation

  • Daniel Wachowiak, 2020. "Genetic Algorithm Approach for Gains Selection of Induction Machine Extended Speed Observer," Energies, MDPI, vol. 13(18), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4632-:d:409633
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    References listed on IDEAS

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    1. Krzysztof Blecharz & Marcin Morawiec, 2019. "Nonlinear Control of a Doubly Fed Generator Supplied by a Current Source Inverter," Energies, MDPI, vol. 12(12), pages 1-15, June.
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    Cited by:

    1. Thyago Estrabis & Gabriel Gentil & Raymundo Cordero, 2021. "Development of a Resolver-to-Digital Converter Based on Second-Order Difference Generalized Predictive Control," Energies, MDPI, vol. 14(2), pages 1-22, January.
    2. Michał Michna & Filip Kutt & Łukasz Sienkiewicz & Roland Ryndzionek & Grzegorz Kostro & Dariusz Karkosiński & Bartłomiej Grochowski, 2020. "Mechanical-Level Hardware-In-The-Loop and Simulation in Validation Testing of Prototype Tower Crane Drives," Energies, MDPI, vol. 13(21), pages 1-25, November.
    3. Piotr Kołodziejek & Daniel Wachowiak, 2022. "Fast Real-Time RDFT- and GDFT-Based Direct Fault Diagnosis of Induction Motor Drive," Energies, MDPI, vol. 15(3), pages 1-14, February.
    4. Daniel Wachowiak, 2021. "A Universal Gains Selection Method for Speed Observers of Induction Machine," Energies, MDPI, vol. 14(20), pages 1-19, October.

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