Intelligent system for monitoring and forecasting the state of electrical equipment based on Microgrid
DOI:
https://doi.org/10.31548/energiya5(81).2025.079Abstract
The concept of building an intelligent system for monitoring and forecasting the technical condition of electrical equipment within a decentralized energy system of the Microgrid type is considered. The architecture of the Predictive Maintenance (PdM) system is proposed, based on the use of sensor networks, Internet of Things (IoT) technologies, artificial intelligence (AI) and big data analytics (Big Data).
A mathematical model for assessing the technical condition of electric motors is developed, which takes into account changes in the main electrical and mechanical parameters (current, voltage, temperature, vibrations, speed). The structure of the PdM process is proposed, which includes the stages of collecting, processing, analyzing data and forming predictive solutions based on machine learning algorithms.
Key words: Microgrid, Predictive Maintenance, monitoring, electric motor, artificial intelligence, Internet of Things, Big Data, modeling, technical condition, energy efficiency
References
1. Melo, J. J. R., Ishraque, M. F., Shafiullah, G. M., & Shezan, S. A. (2023). Centralized monitoring of a cost-efficient PLC-SCADA based islanded microgrid considering dispatch techniques. The Journal of Engineering, 2023(8), 1–11. https://doi.org/10.1049/tje2.12293
2. Inozemtsev G. B., Okushko O. V., Kozyrskyi V. V. (2015). Enerhozberezhennia v systemakh elektropostachannia silskoho hospodarstva [Energy saving in agricultural power supply systems]. Kyiv: CP "Komprint", 151.
3. Li, S., Jiang, B., Wang, X., & Dong, L. (2017). Research and application of a SCADA system for a microgrid. Technologies, 5(2), 12. Avalaible at: https://doi.org/10.3390/technologies5020012
4. Kermani, M., Adelmanesh, B., Shirdare, E., Sima, C. A., Carnì, D. L., & Martirano, L. (2021). Intelligent energy management based on SCADA system in a real Microgrid for smart building applications. Renewable Energy, 171, 1115-1127. https://doi.org/10.1016/j.renene.2021.03.008
5. Benninger, M., Liebschner, M., & Kreischer, C. (2023). Fault detection of induction motors with combined modeling- and machine-learning-based framework. Energies, 16(8), 3429. Avalaible at: https://doi.org/10.3390/en16083429
6. Denisyuk, S. P., Boyko, I. Yu. (2021). Pidvyshchennia enerhoefektyvnosti Microgrid z dyzel-heneratoramy [Increasing the energy efficiency of Microgrid with diesel generators]. Energy: economics, technologies, ecology, 2. Avalaible at: https://doi.org/10.20535/1813-5420.2.2021.247354
7. Nalyvaiko, V., Radko, I., Okushko, O., Bereziuk, A., Antypov, I., & Mrachkovska, N. (2023). Research of roof solar power plant in hot water supply installations. Przegląd Elektrotechniczny, 99(4), 98–101. Avalaible at: https://doi.org/10.15199/48.2023.04.17
8. Radko I. P., Lut M. T., Nalyvaiko V. A., Okushko O. V. (2021). Rozrobka proektu teplovoho punktu navchalnoho korpusu NUBiP Ukrainy [Development of a project for a heating station for the educational building of the NULES of Ukraine]. Energy and Automation,. 86–94.
9. Radko I. P., Nalyvaiko V. A., Okushko O. V., Mishchenko A. V., Antipov I. O. (2019). Research on ways to reduce coolant costs at NUBiP of Ukraine [Research on ways to reduce coolant costs in NULES of Ukraine’. Energy and Automation, 114 – 127.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Energy and Automation
All materials are disseminated under the terms of the Creative Commons Attribution 4.0 International Public License, which permits others to distribute the manuscript with proper acknowledgement of the authorship and the original publication in this journal.
