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.

The findings of the analysis of data on the accident rate of power transformers indicate that one of the main causes of their failures is a decrease in the dielectric strength of the insulation. To reduce failures and extend the service life of power transformers in operation, the issue of enhancing the techniques for assessing the condition of their internal insulation becomes relevant. Currently, when selecting the major insulation of transformers, one takes into account the dependency of the dielectric strength of the oil passage on its width. Experts discuss the issues involved in the choice of major insulation while taking into account the effect of the generalized factor being the volume of the oil passage. The solution to that problem largely depends on the study of the statistical characteristics of the dielectric strength of oil passages of different volumes and the effect rated parameters of transformers have on them. The efficiency of the application of such diagnostic characteristics depends on the extent of studies available on them and the establishment of their standardized parameters. The paper proposes a method for estimating the change in the transformer oil volume in stressed oil passages of major insulation of high-voltage power transformers and statistical characteristics of the dielectric strength of these passages while taking into account the effect of the rated values of capacity and voltage of transformers. It is shown that the degree of effect of transformer technical parameters on the statistical characteristics of the dielectric strength of oil passages depends on the quality of transformer oil, which undergoes a change in operating conditions.

The study develops and implements a methodology for prioritizing power units (PUs) of captive power plants (CPPs) to support the development of maintenance and repair (M&R) programs considering their actual technical condition (TC) and reliability indicators. The proposed approach is based on the joint assessment of the technical condition index (TCI), the consumed technical resource (CTR), and the risk level (RL) of the PUs. To describe the statistical patterns of failures, a two-parameter Weibull distribution is applied, while the temporal change in the TCI is approximated by a linear relationship that accounts for differences between actual and nominal operating conditions. The CTR is defined as an integral characteristic reflecting the deviation between the actual and nominal TCI degradation functions. The RL is evaluated as a function of the probability of failure and the consequences of PU failure. Based on these individual indicators, an integrated priority index is formed to provide an unambiguous ranking of PUs. The methodology was implemented using actual operational data from a fleet of PUs of an energy company. The results demonstrate that using the TCI alone does not fully reflect the actual TC of the PUs, whereas the combined consideration of TC, CTR, and RL enables a more justified formation of M&R programs. The practical significance of the study lies in the possibility of applying the developed methodology for reliability management of PUs at CPPs under resource constraints.

The reliability assessment of power supply systems is complicated by the branched network topology, redundancy, switching operations, post-failure restoration, operating constraints, and uneven equipment degradation. Additional difficulties arise because calculations based on averaged normative parameters often fail to reflect the actual condition of specific components. This review critically compares reliability assessment methods for power supply systems, with particular attention to the technical condition of electrical equipment. Fault tree analysis and the logic–probabilistic method are discussed as structural approaches. Markov models are examined as tools for describing transitions between equipment states. Monte Carlo-based simulation is considered for calculating adequacy deficits and the operational consequences of supply interruptions. Reliability parameterization based on health indices, correction factors, and consumed life is also analyzed. The analysis indicates that no single methodology is equally effective at all problem levels. The main contribution is to separate technical condition parameterization, structural operability modeling, and adequacy assessment into interacting layers. This structure links condition indicators, such as health indices or consumed life, with system-level reliability and deficit indices through element-state probabilities. The review therefore supports a multi-level framework in which technical condition is considered at the element level, structural reliability is evaluated by the logic–probabilistic method, and failure consequences are assessed by simulation.

—At present, one of the most effective methods for detecting dangling bars in squirrel-cage rotor windings of induction motors operating under severe starting conditions is the spectral analysis of stator current in a steady-state operation mode. The existing methods of testing in starting modes are not sensitive enough or their study has not been completed, although it is precisely such methods that allow diagnostics of machines that cannot be inspected in the steady-state operation mode. The purpose of this article is to develop a method for monitoring the state of rotor windings in induction motors operating under severe starting conditions based on the stator current in the starting mode, which is an urgent task. The studies were carried out both on digital models of an induction motor, developed in the Ansys software package, and on an experimental stand with a low voltage supplied to a low-voltage motor to increase the duration of its start. Signal processing is performed in the Matlab software package using the short-time Fourier transform. It is shown that only in the presence of a broken rotor bar, the amplitude of the harmonic component of the order coinciding with the number of motor pole pairs sharply increases at the lower side frequency; this indicates the presence of damage. It is shown that the developed technique for monitoring the condition of rotor windings in induction motors operating under severe starting conditions is more sensitive to broken rotor bars than the existing methods; this makes it possible to timely detect damage to rotor windings and prevent sudden failures of induction motors.

The review presents a comprehensive review of the influence of power quality indicators and operating conditions at industrial enterprises on the technical condition and reliability of electrical equipment. Harmonic distortion, voltage fluctuations and sags, load surges, overvoltages, and voltage unbalance are considered factors that increase thermal, electrical, and mechanical stresses in transformers, induction motors, cable lines, and overhead power lines. It is shown that these disturbances can increase RMS currents, additional losses, hot-spot temperature, vibration, and insulation aging rate, reducing equipment service life and increasing failure probability. The review links power quality disturbances with thermal aging models, remaining useful life assessment, and probabilistic reliability models, including the Weibull distribution. It is established that a correct remaining service life assessment requires considering not only individual disturbances but also the combined influence of voltage and current quality, load conditions, ambient temperature, and humidity. Particular attention is paid to modern monitoring and forecasting technologies, including IoT systems, multi-agent models, machine learning, and predictive diagnostics. These technologies enable the transition from scheduled maintenance to continuous multiparameter monitoring. A structure for quantitative risk assessment and practical recommendations for predictive maintenance of industrial electrical equipment are proposed.