Review of Operating Conditions, Diagnostic Methods, and Technical Condition Assessment to Improve Reliability and Develop a Maintenance Strategy for Electrical Equipment

Alexander Nazarychev, Iliya Iliev (), Daniel Manukian, Hristo Beloev, Konstantin Suslov () and Ivan Beloev
Additional contact information
Alexander Nazarychev: Department of Electric Power Engineering and Electromechanics, Empress Catherine II Saint Petersburg Mining University, 199106 St Petersburg, Russia
Iliya Iliev: Department of Heat, Hydraulics and Environmental Engineering, “Angel Kanchev” University of Ruse, 7017 Ruse, Bulgaria
Daniel Manukian: Department of Electric Power Engineering and Electromechanics, Empress Catherine II Saint Petersburg Mining University, 199106 St Petersburg, Russia
Hristo Beloev: Department of Agriculture Machinery, “Angel Kanchev” University of Ruse, 7017 Ruse, Bulgaria
Konstantin Suslov: Department of Hydropower and Renewable Energy, National Research University “Moscow Power Engineering Institute”, 111250 Moscow, Russia
Ivan Beloev: Department of Transport, “Angel Kanchev” University of Ruse, 7017 Ruse, Bulgaria

Energies, 2025, vol. 18, issue 21, 1-40

Abstract: In the context of increasing demands for the reliability and efficiency of electrical complexes and systems, the problem of assessing and monitoring the technical condition (TC) of electrical equipment is becoming particularly relevant. This review is devoted to a comprehensive analysis of the factors affecting the performance of electrical equipment and modern methods for diagnosing its TC. The review article examines in detail the impact of various operational factors, including climatic conditions (temperature fluctuations, humidity, contamination) and electrical equipment operating modes. Special attention is paid to modern methods of technical diagnostics, such as thermographic diagnostics, vibration diagnostics, and chromatographic analysis of dissolved gases, which make it possible to identify defects and predict failures at early stages of their development. A significant part of the review is devoted to modern approaches to predicting the durability indicators of electrical equipment using mathematical modeling and neural networks. The advantages of a condition-based maintenance (CBM) and repair strategy, based on assessing the actual TC of the equipment, are analyzed in detail and compared with the strategy of scheduled preventive maintenance. This review particularly emphasizes the importance of integrating digital technologies, including the internet of things (IoT), digital twins (DT), and intelligent diagnostic monitoring systems, to create effective systems for predicting and managing TC. The review demonstrates that a comprehensive consideration of the actual TC of electrical equipment and its operating conditions can significantly increase the reliability of power systems, optimize maintenance and repair costs, and extend the service life of electrical equipment under various intensities of impacting operational factors.

Keywords: technical condition; reliability indicators; operating conditions; diagnostic methods; maintenance; electrical equipment (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/21/5832/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/21/5832/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:21:p:5832-:d:1787885

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().