2024, Vol. 11, No. 1. - go to content...

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DOI: 10.15862/19INOR124 (https://doi.org/10.15862/19INOR124)

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Artemyev V.S., Mokrova N.V. Automated methods for analyzing and predicting self-oscillations in agricultural systems. Russian journal of resources, conservation and recycling. 2024; 11(1). Available at: https://resources.today/PDF/19INOR124.pdf (in Russian). DOI: 10.15862/19INOR124

Automated methods for analyzing and predicting self-oscillations in agricultural systems

Artemyev Victor Stepanovich
Biotech University, Moscow, Russia
E-mail: artemyevvs@mgupp.ru
ORCID: https://orcid.org/0000-0002-0860-6328
RSCI: https://elibrary.ru/author_profile.asp?id=806146

Mokrova Nataliya Vladislavovna
National University of Science and Technology «MISIS», Moscow, Russia
E-mail: mokrovanv@mgupp.ru
ORCID: https://orcid.org/0000-0002-8444-2935
WoS: https://www.webofscience.com/wos/author/rid/ABB-46-05-2021
SCOPUS: https://www.scopus.com/authid/detail.url?authorId=41762121300

Abstract. The article presents the study of automated methods used for analysis and prediction of auto oscillations in agricultural systems. Auto oscillations arising in control systems and electronics have a negative impact on the reliability of electrical equipment, effective control of the operation of units and systems. The influence of optimization of parameters: frequency and amplitude of auto oscillations, which allow to use effectively technological resources, increase the service life of equipment, reduce the cost of repair and replacement, is shown. The obtained value of the amplitude of auto oscillations can be used to monitor the level of the control signal, also the estimation of amplitude shows the degree of impact on the environment or other components of the system. As a result of research, the automated method for analysis and forecasting of auto oscillations in electric drives is offered, principles and technologies of occurrence of unstable modes are studied, the practical example of application of the method of forecasting of auto oscillations in the technological systems having in structure the electric drive. Methods based on data processing, mathematical modeling and machine learning are used. Particular attention is paid to harmonic linearization for revealing the conditions of occurrence and control of auto oscillations. The proposed methods are based on modern data processing algorithms, artificial intelligence, machine learning and a deep understanding of technological processes.

The proposed models and methods ensure the creation of stable and reliable solutions, along with the identification of general regularities, minimizing the influence of random factors, which emphasizes the importance of using the proposed techniques. Automated methods of analysis and forecasting of auto oscillations increase the efficiency of resource use, allow to optimize production processes, and make technological systems of agriculture more resistant to changes in external conditions.

Keywords: prediction autooscillations; harmonic linearization; amplitude; frequency; correction parameters; machine learning; resources

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