Intelligent system for automated design of energy-efficient facilities in the agro-industrial complex with built-in diagnostics of electrical machines

Abstract

The aim of the study was to substantiate the effectiveness of an intelligent system for automated design of energy-efficient facilities in the agro-industrial complex (AIC) with integrated diagnostics of electric drives. The methodology included experimental testing of asynchronous electric motors (1.5-7.5 kW), numerical modelling using finite element and finite difference methods, and optimisation based on a genetic algorithm and particle swarm. Data analysis was performed using Student's t-test, analysis of variance, and principal component analysis. The results showed that in faulty electric motors, the current increased by 20%, the power factor decreased from 0.87 to 0.74, the vibration level exceeded that of serviceable samples by three times, and the temperature of the windings increased from 65 to 92 degrees Celsius. Numerical modelling showed a 9% reduction in energy consumption, a 17-degree Celsius decrease in temperature, a 16% increase in power factor, a 47% reduction in vibrations, and an increase in efficiency from 85 to 92%. Statistical analysis confirmed the reliability of the results, and the “serviceable/damaged” classification model achieved high accuracy with an area under the curve of 0.94. The scientific novelty lies in the first-ever combination of structural optimisation of electric drive parameters with built-in technical condition analysis in a single CAD/FEM environment. The developed system provides early detection of deviations in motor operation, reduction of energy consumption and improvement of reliability without loss of productivity. The practical outcome is the creation of an integrated tool that increases the energy efficiency of AIC production lines and can be implemented in energy audit and maintenance processes.