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Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 28 September 2025 | Viewed by 1418

Special Issue Editors

Dr. Xiaozhen Zhao

E-Mail Website
Guest Editor
School of Electrical Engineering, Hebei University of Technology, Tianjin, China
Interests: electrical equipment intelligent diagnosis and health management; high-reliability pulsed power technology; power equipment life-cycle management; advanced detection technology development
Dr. Yiming Zang

E-Mail Website
Guest Editor
Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
Interests: condition monitoring of power equipment; study on the mechanism of high voltage discharge; intelligent diagnosis and condition prediction of power equipment based on artificial intelligence algorithm

Special Issue Information

Dear Colleagues,

The growing complexity of electrical systems in modern industries and infrastructure makes the reliability and efficiency of electrical equipment more crucial than ever. Aging equipment, system integration, and the increasing demand for energy efficiency require advanced diagnostic methods, predictive maintenance strategies, and robust management techniques. Proactive maintenance is a critical component in ensuring the longevity and functionality of electrical systems across various applications, including power generation, transportation, and industrial operations.

This Special Issue aims to provide an overview of recent advancements in the diagnostic analysis, strategic management, and proactive maintenance of electrical equipment. We welcome contributions that explore innovative approaches to condition monitoring, fault detection, predictive maintenance, and the optimization of asset management strategies. This Special Issue seeks to bridge the gap between innovative research and its practical application in real-world settings.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Advanced diagnostic techniques and tools for fault detection and prognosis in electrical systems.
  • Predictive maintenance strategies for critical electrical infrastructure and industrial applications.
  • Data-driven approaches to condition monitoring using machine learning and artificial intelligence.
  • Reliability-centered maintenance (RCM) and risk-based asset management strategies.
  • Lifecycle management and optimization of electrical equipment.
  • Applications of real-time monitoring and remote diagnostics in electrical equipment.
  • The role of big data and IoT in proactive maintenance and fault prevention.
  • The development and application of fault-tolerant strategies in electrical systems.
  • Case studies on the successful implementation of advanced maintenance techniques in energy, transportation, and industrial sectors.

We welcome high-quality research that contributes to the development of innovative maintenance strategies and management frameworks, which will ultimately enhance the performance and sustainability of electrical systems.

Dr. Xiaozhen Zhao
Dr. Yiming Zang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fault detection
  • predictive maintenance
  • condition monitoring
  • electrical equipment reliability
  • asset management
  • proactive maintenance strategies
  • lifecycle management
  • machine learning in maintenance
  • data-driven diagnostic tools
  • real-time monitoring
  • smart grids
  • equipment failure prediction
  • advanced diagnostic techniques
  • Industrial Internet of Things
  • big data in power systems
  • energy infrastructure management
  • electrical system health monitoring

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

20 pages, 7885 KiB  
Article
Fault Diagnosis Method for Transformer Winding Based on Differentiated M-Training Classification Optimized by White Shark Optimization Algorithm
by Guochao Qian, Kun Yang, Jin Hu, Hongwen Liu, Shun He, Dexu Zou, Weiju Dai, Haozhou Wang and Dongyang Wang
Energies 2025, 18(9), 2290; https://doi.org/10.3390/en18092290 - 30 Apr 2025
Abstract
Transformers, serving as critical components in power systems, are predominantly affected by winding faults that compromise their operational safety and reliability. Frequency Response Analysis (FRA) has emerged as the prevailing methodology for the status assessment of transformer windings in contemporary power engineering practice. [...] Read more.
Transformers, serving as critical components in power systems, are predominantly affected by winding faults that compromise their operational safety and reliability. Frequency Response Analysis (FRA) has emerged as the prevailing methodology for the status assessment of transformer windings in contemporary power engineering practice. To mitigate the accuracy limitations of single-classifier approaches in winding status assessment, this paper proposes a differentiated M-training classification algorithm based on White Shark Optimization (WSO). The principal contributions are threefold: First, building upon the fundamental principles of the M-training algorithm, we establish a classification model incorporating diversified classifiers. For each base classifier, a parameter optimization method leveraging WSO is developed to enhance diagnostic precision. Second, an experimental platform for transformer fault simulation is constructed, capable of replicating various fault types with programmable severity levels. Through controlled experiments, frequency response curves and associated characteristic parameters are systematically acquired under diverse winding statuses. Finally, the model undergoes comprehensive training and validation using experimental datasets, and the model is verified and analyzed by the actual transformer test results. The experimental findings demonstrate that implementing WSO for base classifier optimization enhances the M-training algorithm’s diagnostic precision by 8.92% in fault-type identification and 8.17% in severity-level recognition. The proposed differentiated M-training architecture achieves classification accuracies of 98.33% for fault-type discrimination and 97.17% for severity quantification, representing statistically significant improvements over standalone classifiers. Full article
(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment)
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23 pages, 22852 KiB  
Article
Numerical Analysis and Experimental Verification of Optical Fiber Composite Overhead Ground Wire (OPGW) Direct Current (DC) Ice Melting Dynamic Process Considering Gap Convection Heat Transfer
by Shuang Wang, Long Cheng, Bo Tang, Wangsheng Xu and Zheng Wang
Energies 2025, 18(8), 2090; https://doi.org/10.3390/en18082090 - 18 Apr 2025
Viewed by 182
Abstract
An accurate analysis of the dynamic process of ice melting in an optical fiber composite overhead ground wire (OPGW) is of great reference significance for the selection of an ice melting current and the formulation of an ice melting strategy. Existing analytical models [...] Read more.
An accurate analysis of the dynamic process of ice melting in an optical fiber composite overhead ground wire (OPGW) is of great reference significance for the selection of an ice melting current and the formulation of an ice melting strategy. Existing analytical models for the dynamic process of DC ice melting in an OPGW ignore the gap convective heat transfer after the formation of the air gap between the ground wire and the ice layer, and lack the study of the dynamic process of the phase transition of the ice layer. To this end, a finite element model of the DC ice melting process of OPGW was established by introducing the mushy zone constant to consider the influence of the convective heat transfer in the gap, and at the same time, the apparent heat capacity method was used to simulate the changes of the physical property parameters of the melted ice layer. The dynamic process of the ice layer phase transition and OPGW temperature rise during ice melting are calculated, and the effects of the half-width of phase transition interval dT and the mushy zone constant Am on the DC ice melting process are summarized and analyzed. The accuracy of the OPGW DC ice melting model is verified by conducting DC ice melting experiments. The results show that during the ice melting process, the gap convection heat transfer mainly affects the temperature distribution of the air gap between the ice layer and the OPGW as well as the location of the phase transition interface, and the width of the air gap at the same height below the OPGW increases by about 3 mm after considering the gap convection; the half-width of phase transition interval, dT, mainly affects the location of the phase transition interface and the temperature rise of the modeled heat source, OPGW, while the mushy zone constant, Am, mainly affects the temperature distribution in the mushy zone, the air gap region. The elliptical phase transition cross-section formed by the OPGW DC ice melting experiment is consistent with the shape of the ice melting simulation model results, and the measured temperature rise curves of the OPGW during DC ice melting are in good agreement with the simulation results, with a maximum difference of about 3.5 K in temperature and 10 min in ice melting time, but the overall trend is consistent, all showing as increasing first and then decreasing. Full article
(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment)
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12 pages, 3837 KiB  
Article
Evaluation on the Long-Term Operational Reliability of Closing Springs in High-Voltage Circuit Breakers
by Mingkun Yang, Liangliang Wei, Pengfeng Qiu, Guangfu Hu, Kun Yang, Xiaohui He, Zhaoyu Peng, Fangrong Zhou, Yun Zhang, Jie Luo and Xuetong Zhao
Energies 2025, 18(7), 1806; https://doi.org/10.3390/en18071806 - 3 Apr 2025
Viewed by 230
Abstract
As a key energy storage component in high-voltage circuit breakers, closing springs are susceptible to stress relaxation, resulting in a decline in closing performance due to high operational loads, prolonged usage, and environmental factors. In this work, the 60Si2CrVA alloy steel springs used [...] Read more.
As a key energy storage component in high-voltage circuit breakers, closing springs are susceptible to stress relaxation, resulting in a decline in closing performance due to high operational loads, prolonged usage, and environmental factors. In this work, the 60Si2CrVA alloy steel springs used in 110 kV high-voltage circuit breakers were utilized to study their mechanical behaviors under various temperatures, salt spray corrosion, and repeated closing operations. It is found that the conditions of salt spray corrosion, and repeated closing operations demonstrate a slight impact on its stress loss, while the operation temperature above 70 °C will result in an apparent increase in the stress loss rate. A threshold for closing failure related to the spring’s stress loss rate was established, and a life prediction method based on an improved Arrhenius acceleration model was proposed. The results indicate that the calculated service life of the spring is approximately 27.09 years based on the stress loss rate threshold of 4.5%. This work provides a novel method to evaluate the long-term operational state and service life of closing springs in high-voltage circuit breakers. Full article
(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment)
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14 pages, 1566 KiB  
Article
Risk Assessment Model for Converter Transformers Based on Entropy-Weight Analytic Hierarchy Process
by Guochao Qian, Weiju Dai, Dexu Zou, Haoruo Sun, Hanting Zhang and Jian Hao
Energies 2025, 18(7), 1757; https://doi.org/10.3390/en18071757 - 1 Apr 2025
Viewed by 244
Abstract
As a critical component in voltage–current conversion and power transmission within HVDC systems, the risk assessment of converter transformers plays a significant role in ensuring their operational safety and enhancing the reliability of the power supply. To address the issues of the incomplete [...] Read more.
As a critical component in voltage–current conversion and power transmission within HVDC systems, the risk assessment of converter transformers plays a significant role in ensuring their operational safety and enhancing the reliability of the power supply. To address the issues of the incomplete characteristic parameters and limited fault data used for model training in existing transformer evaluation models, this paper develops a risk assessment model for converter transformers based on the entropy-weighted analytic hierarchy process (AHP). Firstly, in accordance with relevant standards in the power industry and existing experimental research, 14 ‘electrical–thermal–mechanical’ multi-dimensional characteristic parameters, including partial discharge, dissolved gases in oil, and hot spot temperature rise, are selected to effectively reflect the risk state of converter transformers. The risk state is then categorized into four levels. Next, the AHP, which uses a subjective weighting method, is combined with the entropy-weight method, an objective weighting approach, to construct the risk assessment model for converter transformers based on the entropy-weighted AHP. Finally, the effectiveness of the model is validated through four case studies of converter transformers. The results indicate that the risk assessment model proposed in this paper can accurately and effectively reflect the risk state of transformers at different levels, providing valuable guidance for the development of maintenance strategies for converter transformers. Full article
(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment)
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17 pages, 4831 KiB  
Article
Harmonic Current Effect on Vibration Characteristics of Oil-Immersed Transformers and Their Experimental Verification
by Dexu Zou, Jian Hao, Weiju Dai, Guochao Qian, Haoruo Sun and Jing Xu
Energies 2025, 18(7), 1673; https://doi.org/10.3390/en18071673 - 27 Mar 2025
Viewed by 182
Abstract
Harmonic currents can intensify transformer vibrations, seriously threatening their mechanical stability and safe operation. Drawing upon this foundation, the present paper undertakes a thorough simulation and experimental investigation into the vibration characteristics of transformers under diverse harmonic current scenarios. Initially, a multi-field coupling [...] Read more.
Harmonic currents can intensify transformer vibrations, seriously threatening their mechanical stability and safe operation. Drawing upon this foundation, the present paper undertakes a thorough simulation and experimental investigation into the vibration characteristics of transformers under diverse harmonic current scenarios. Initially, a multi-field coupling model incorporating both “electromagnetic and structural forces” was developed to simulate and analyze how the vibration acceleration of a transformer is distributed under varying harmonic currents. Subsequently, a specialized transformer harmonic loading and vibration measurement platform was constructed to validate the multi-physical-field vibration simulation. Finally, through a rigorous experimental analysis of transformer vibrations under harmonic currents, this research elucidates the variation patterns of characteristic vibration parameters of transformers under different harmonic currents. The results demonstrate that as the proportion of harmonic current grows, the mean winding vibration acceleration escalates following a power-function law. With increasing harmonic current frequency, the vibration acceleration augmentation in high-voltage (HV) windings exceeds that which is observed in low-voltage (LV) windings. Empirical validation confirms that the discrepancy between the measured and simulated acceleration increases remains within 5%, indicating the effectiveness and reliability of the simulation method. Experimental findings reveal that as the harmonic current content increases, six vibration characteristic parameters—including root mean square value, absolute average value, peak-to-peak value, and mean frequency—exhibit a pronounced upward trend. Furthermore, harmonic currents significantly increase the spectral dispersion and high-frequency components of the vibration signal. These research findings provide valuable references for transformer operation, maintenance, and anti-vibration design strategies. Full article
(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment)
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