Q-tables formation method for automated monitoring of electromechanical converters parameters with application of linear integral criterion

Author:

Malev N. A.1,Pogoditsky O. V.1,Malacion A. S.2

Affiliation:

1. Kazan State Power Engineering University

2. LLC «Stek Master»

Abstract

In the process of functioning working sets with electromechanical converters included in their composition, it is necessary to take into account the influence of endogenous and exogenous disturbances that cause deviations of the parameters of electric machines from the nominal values given by the manufacturer in the appropriate documentation. These deviations of the parameters, even those within the permissible range of changes, have a noticeable effect on the quality of functioning of electromechanical converters and working sets as a whole. During the life cycle of the work of electromechanical converters, their parameters change as a result of natural wear and senescence, which necessitates continuous or periodic analysis and monitoring of the state objects under study. The paper considers a method based on the calculation of the linear integral criterion Q and the formation of Q – tables, which allows monitoring the functioning of electromechanical converters with unstable parameters during operation as part of working sets. Simulink – models of linear integral criterion calculation system and system of automated monitoring of electromechanical DC converter parameters are presented, which allow estimating unstable parameters. In these models static characteristics are implemented in tabular form reflecting the dependencies between the parameters of the electromechanical converters and the linear integral criterion. The results of the study allow us to obtain estimates of changes in the unstable parameters of electromechanical DC converters with the required accuracy.

Publisher

Kazan State Power Engineering University

Subject

Pharmacology (medical),Complementary and alternative medicine,Pharmaceutical Science

Reference20 articles.

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3. Tonoyan SA, Baldin AV, Eliseev DV. Тechnical State Prediction of Electronic Systems with Adaptive Parametric Models. Herald of the Bauman Moscow State Tech. Univ., Instrum. Eng., 2016;6:115- 125.

4. Malev NA, Mukhametshin AI, Pogoditsky OV, et al. Experimental-analytical identification of a mathematical model of a dc motor using the least squares method. Power engineering: research, equipment, technology. 2019;21(4):113-122.

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