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

Daniel Wachowiak

doi:10.3390/en13184632

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

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.

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Category:: Articles
Type:: artykuły w czasopismach
Published in:: ENERGIES no. 13,
ISSN: 1996-1073
Language:: English
Publication year:: 2020
Bibliographic description:: Wachowiak D.: Genetic Algorithm Approach for Gains Selection of Induction Machine Extended Speed Observer// ENERGIES -Vol. 13,iss. 18 (2020), s.4632-
DOI:: Digital Object Identifier (open in new tab) 10.3390/en13184632
Verified by:: Gdańsk University of Technology