STUDY OF PERIODIC PROCESSES TAKING INTO ACCOUNT THE UNCERTAINTY STATE FACTOR OF THE ANALYZED PARAMETER

The paper describes the approach and methodology for the numerical assessment of the quality of periodic processes. With regard to their particular case the processes of selfoscillations, on the basis of the proposed approach, the problem of optimization of the control system is formulated and possible ways of solving it are shown. The practical implementation of the approach is possible when synthesizing a control system, as well as in the variant of automatic adaptive control during its operation. The proposed solutions are based on the use of methods of the theory of entropy potentials. The implementation of the proposed solutions will improve the quality of monitoring of periodic processes and the efficiency of regulation systems in selfoscillation modes.

High-frequency periodic processes in two-winding power transformers

Purpose. Mathematical modeling of high-frequency periodic processes in winding power transformers to improve the technology of their design and operation. Methodology. The methods of the formation of mathematical models for the research of high-frequency periodic processes in transformers and methods of solving systems of partial differential equations are applied. Findings. The mathematical model for the research of high-frequency periodic processes in two-winding transformers, with adequate considering of electromagnetic connections of windings and structural parameters of transformers, is created. Originality. To form a mathematical model for the research of high-frequency periodic processes, a substitute scheme of a two-windings transformer, taking into account the parameters of the electric and magnetic circuits of windings and electromagnetic connections between them, is proposed. Practical value. The mathematical model, which allows analyzing the voltage distribution in the transformer windings for high-frequency periodic processes in windings, and adjusting their insulating abilities, is created.

Prediction in Chaotic Environments Based on Weak Quadratic Classifiers

The problem of prediction in chaotic environments based on identifying analog situations in arrays of retrospective data are considered. Traditional recognition schemes are ineffective and form weak classifiers in cases where the system component of the observed process is represented by a non-periodic oscillatory time series (realization of chaotic dynamics). The objective is to develop a system of such classifiers, which allows for improvements in the quality of forecasts for non-stationary dynamics in flow processes. The introduced technique can be applied for the prediction of oscillatory non-periodic processes with non-stationary noise, i.e., dependence of different relay frequencies, external electric potential and microchannel width in an electrokinetic micromixer.