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A Universal Gains Selection Method for Speed Observers of Induction Machine

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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

Properties of state observers depend on proper gains selection. Each method of state estimation may require the implementation of specific techniques of finding those gains. The aim of this study is to propose a universal method of automatic gains selection and perform its verification on an induction machine speed observer. The method utilizes a genetic algorithm with fitness function which is directly based on the impulse response of the observer. System identification using least-squares estimation is implemented to determine the dynamic properties of the observer based on the estimation error signal. The influence of sampling time as well as signal length on the system identification has been studied. The results of gains selection using the proposed method have been compared with results obtained using the approach based on the placement of the poles of linearized estimation error equations. The introduced method delivers results comparable with analytical methods and does not require prior preparation specific to the implemented speed observer, such as linearization.

Suggested Citation

  • Daniel Wachowiak, 2021. "A Universal Gains Selection Method for Speed Observers of Induction Machine," Energies, MDPI, vol. 14(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6790-:d:658807
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Daniel Wachowiak, 2020. "Genetic Algorithm Approach for Gains Selection of Induction Machine Extended Speed Observer," Energies, MDPI, vol. 13(18), pages 1-24, September.
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    Cited by:

    1. 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.

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