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Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment—2nd Edition

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

Deadline for manuscript submissions: 10 September 2026 | Viewed by 2732

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Special Issue Editors

Dr. Xiaozhen Zhao

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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
Special Issues, Collections and Topics in MDPI journals

Dr. Yiming Zang

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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
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Dr. Mohamad Ghaffarian

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Guest Editor

Department of Electrical Sustainable Energy, Delft University of Technology, 2628 CD Delft, The Netherlands
Interests: high voltage, aging of insulation material; high-voltage power electronics-based test sources; diagnostics and monitoring; medium-frequency transformers; power transformers; power cables; FEM-based modeling
Special Issues, Collections and Topics in MDPI journals

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. The challenges of 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 present and disseminate the latest advancements in the diagnostic analysis, strategic management, and proactive maintenance of electrical equipment. We invite contributions that explore innovative approaches to condition monitoring, fault detection, predictive maintenance, and the optimization of asset management strategies. This issue seeks to bridge the gap between cutting-edge research and its practical applications in real-world settings.

Topics of interest for publication include, but are not limited to:

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.
Development and application of fault-tolerant strategies in electrical systems.
Case studies on successful implementations 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, ultimately improving the performance and sustainability of electrical systems.

Dr. Xiaozhen Zhao
Dr. Yiming Zang
Dr. Mohamad Ghaffarian
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 250 words) can be sent to the Editorial Office for assessment.

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 (IoT)
big data in power systems
energy infrastructure management
electrical system health monitoring

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Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment in Energies (19 articles)

Published Papers (6 papers)

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Research

15 pages, 2125 KB

Open AccessArticle

Multi-Scale Assessment of Transformer Inrush Suppression by Pre-Magnetization Based on Clarke–Wavelet Energy Spectrum

by Chenlei Li, Junchi He, Shoujiang He, Shaofan Gu, Chenhao Ma, Xianglong Gu and Xiaozhen Zhao

Energies 2026, 19(9), 2070; https://doi.org/10.3390/en19092070 - 24 Apr 2026

Viewed by 265

Abstract

Transformers serve as crucial hubs for power transmission, but during no-load energization, the nonlinear magnetization of their cores frequently induces extreme magnetizing inrush currents. Current suppression methods encounter challenges regarding transient feature extraction and excessive circuit complexity. To overcome these limitations, this study develops a high-fidelity model of a 100 kVA transformer using MATLAB/Simulink to investigate the interaction between residual flux and the closing angle. Extensive simulations were executed across a closing phase angle range of 0° to 360° and a residual flux domain of −0.8 p.u. to 0.8 p.u. Furthermore, this study utilizes Wavelet and Clarke transforms to extract characteristic parameters and quantitatively analyze the transients within the energy domain, enabling a multi-scale assessment of the mitigation efficacy based on these extracted features. The analytical results demonstrate that an optimal pre-magnetization distribution of −0.8 p.u. for Phase A, 0 p.u. for Phase B, and 0.8 p.u. for Phase C, coupled with a target closing angle of 330°, achieves the best suppression. This strategy strictly clamps the peak inrush current to 1.5 times the rated current, significantly outperforming conventional demagnetization alone. Consequently, this highly pronounced mitigation effect provides robust support for reliable transformer protection and overall power grid security. Full article

(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment—2nd Edition)

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16 pages, 8247 KB

Open AccessArticle

Study on the DC Discharge Model of Insulators Polluted by Typical Components Based on Effective Salt Deposit Density

by Wei Zhang, Shaoming Pan, Laisheng Zhong, Liangyuan Chen and Yuan Ma

Energies 2026, 19(6), 1531; https://doi.org/10.3390/en19061531 - 19 Mar 2026

Viewed by 359

Abstract

Pollution flashover accidents of transmission line insulators have a wide impact and low reclosing success rates, posing a serious threat to the safe and stable operation of the power grid. The existing pollution discharge and flashover models of insulator based on equivalent salt deposit density (ESDD) present significant differences from the actual situation. To address this issue, the conductivity of electrolyte solutions experiments is carried out in this paper, and the quantitative functional relationship between conductivity and concentration of typical components is obtained. On this basis, the concept of effective salt deposit density (SDD_e) is introduced to characterize the actual mass of pollution participating in surface conduction per unit area. A DC discharge dynamic model for polluted insulators is established and verified based on SDD_e combined with the discharge development process. Research results indicate that the average difference between the calculated flashover voltage and experimental value is less than 7%. The deviation of flashover voltage between the SDD_e basis model and measured salt deposit density (SDD_m) basis value increases with the increasing proportion of slightly soluble components. With the increase of insulator surface water adhesion, the flashover voltage obtained by the proposed model decreases while the corresponding SDD_m basis value remains constant. The effects of factors such as slightly soluble pollution and surface water adhesion are considered in the proposed model sufficiently. The application of the model based on SDD_e can improve the accuracy of the insulator discharge process and flashover voltage prediction, especially for the complex pollution area. During the generation and propagation of the arc, the leakage current under SDD_m is relatively higher and the pollution layer resistance is lower compared to that under SDD_e; the variations in the pollution layer resistance and leakage current with arc development under SDD_m do not adequately reflect the actual conditions. Full article

(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment—2nd Edition)

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21 pages, 4482 KB

Open AccessArticle

Lightweight Defect Detection in Substations with Multi-Scale Features and Network Pruning

by Tong Zhang, Tian Wu and Zhenhui Ouyang

Energies 2026, 19(5), 1163; https://doi.org/10.3390/en19051163 - 26 Feb 2026

Viewed by 446

Abstract

With the increasing adoption of intelligent inspection systems for substation equipment, massive amounts of data are being generated. To address the challenge of balancing detection accuracy and lightweight deployment in current object detection models, this paper proposes YOLOv10-SPD (Substation Power Defect), a high-precision yet lightweight improved model tailored for substation defect detection. Compared to existing methods, the proposed model introduces multiple innovations in structural design and module fusion. (1) A Feature Modulation Module is proposed to significantly enhance the model’s ability to perceive and model defect details. (2) A hybrid module integrating structural information and channel attention is designed to efficiently reconstruct and represent feature maps. (3) A Multi-Scale Context Modeling Module is developed, leveraging shared convolutional kernels to achieve compact expression of multi-scale semantic information. (4) An Efficient Detection Head adopts a hierarchical semantic fusion strategy, further improving recognition accuracy for small and multi-scale targets. (5) A Weight-Magnitude-Based Hierarchical Pruning Strategy is introduced to compress model size and boost inference efficiency while maintaining accuracy. Experiments on a public substation defect dataset demonstrate that the proposed method achieves 94.11% mAP@0.5, outperforming the baseline YOLOv10n by 5.14%, while reducing model parameters by 76.09% and computational costs by 38.82%. The model achieves higher detection accuracy with lower computational overhead, effectively meeting the requirements for efficient and accurate substation defect detection, demonstrating strong practical applicability. Full article

(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment—2nd Edition)

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23 pages, 5452 KB

Open AccessArticle

Study on Radial Stability of Power Transformer Winding Considering Conductor Transposition Structure and Brace Support State

by Shuang Wang, Yizhen Luo, Yuen Wen, Bo Tang and Jianwen Gong

Energies 2026, 19(5), 1147; https://doi.org/10.3390/en19051147 - 25 Feb 2026

Viewed by 411

Abstract

Continuously transposition conductors are widely used in power transformers, but there is a problem of overestimation due to simplification when evaluating the radial stability of windings. In this study, a refined multi-turn finite element model of 220 kV transformer winding is established, considering the conductor transposition structure and support stiffness. Compared with the traditional model, the effects of support degradation, local support failure and short-circuit cumulative effects are studied. The results show that under the same stiffness, the critical buckling load of the simplified model is 8.8% higher than that of the refined model, and the maximum stress is 32.2% lower. Reducing the support stiffness can reduce the critical buckling load, and the maximum stress will be transferred from the support to the transposition area. Local brace failure will further reduce the critical buckling load. After 11 times of short-circuit impacts, the residual stress and plastic displacement in the transposition area reached 38.9 MPa and 0.73 mm, respectively. The establishment of the refined model lays the foundation for the accurate evaluation of radial stability and the structural design of the transformer. Full article

(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment—2nd Edition)

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17 pages, 3179 KB

Open AccessArticle

Collaborative Suppression Strategy for AC Asymmetric Faults in Offshore Wind Power MMC-HVDC Systems

by Xiang Lu, Chenglin Ren, Shi Jiao, Jie Shi, Weicheng Li and Hailin Li

Energies 2026, 19(2), 365; https://doi.org/10.3390/en19020365 - 12 Jan 2026

Viewed by 457

Abstract

When offshore wind power is connected to a grid via Modular multilevel converter-based High Voltage Direct Current (MMC-HVDC), the sending-end alternating current (AC) system is susceptible to asymmetrical faults. These faults lead to overcurrent surges, voltage drops, and second harmonic circulating currents, which seriously threaten the safe operation of the system. To quickly suppress fault current surges, achieve precise control of system variables, and improve fault ride-through capability, this study proposes a collaborative control strategy. This strategy integrates generalized virtual impedance current limiting, positive- and negative-sequence collaborative feedforward control, and model-predictive control-based suppression of arm energy and circulating currents. The positive- and negative-sequence components of the voltage and current are quickly separated by extending and decoupling the decoupled double synchronous reference frame phase-locked loop (DDSRF-PLL). A generalized virtual impedance with low positive-sequence impedance and high negative-sequence impedance was designed to achieve rapid current limiting. Simultaneously, negative-sequence current feedforward compensation and positive-sequence voltage adaptive support are introduced to suppress dynamic fluctuations. Finally, an arm energy and circulating current prediction model based on model predictive control (MPC) is established, and the second harmonic circulating currents are precisely suppressed through rolling optimization. Simulation results based on PSCAD/EMTDC show that the proposed control strategy can effectively suppress the negative-sequence current, significantly improve voltage stability, and greatly reduce the peak fault current. It significantly enhances the fault ride-through capability and operational reliability of offshore wind power MMC-HVDC-connected systems and holds significant potential for engineering applications. Full article

(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment—2nd Edition)

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13 pages, 3207 KB

Open AccessArticle

Considering Moisture Intrusion Evolution Law of Insulation Performance of High-Voltage AC XLPE Cables

by Shili Liu and Guanbo Zong

Energies 2026, 19(1), 138; https://doi.org/10.3390/en19010138 - 26 Dec 2025

Viewed by 412

Abstract

The outer sheaths of cables can be damaged by factors, such as mechanical stress, chemical corrosion, and aging, leading to moisture intrusion. This seriously threatens cable insulation performance and may even induce discharge accidents. Based on the corrugated aluminum sheath structure of the cables and moisture diffusion mechanism, the moisture intrusion (moisture absorption) process can be divided into three stages: water-blocking tape adsorption, air-gap wetting, and main insulation diffusion. First, through experimental tests, key electrical parameters such as capacitance, dielectric constant, and dielectric loss of 66 kV cables and XLPE main insulation samples in different moisture absorption stages were obtained. Furthermore, using finite element simulation, theoretical analysis and verification of the parameter variation characteristics of the cable were conducted by adjusting the moisture content and varying the moisture-affected positions. The results show that the electrical parameters of the cable body change most significantly in the third stage of moisture absorption: when the moisture absorption degree increases by 0.01%, the cable body capacitance increases by 1.2% and the insulation resistance decreases by 3.7%; for the XLPE insulation samples, when the moisture absorption degree increases by 0.25%, the relative dielectric constant increases by 0.7%, the conductivity increases by 1.4%, and the dielectric loss increases by a factor of 1.6 times at lower frequencies. In addition, the changes in the main insulation parameters were only related to the moisture content and were not affected by moisture distribution. Full article

(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment—2nd Edition)

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