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Electronics
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Advances in Condition Monitoring, Diagnosis, and Prognostics for Power Equipment
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Advances in Condition Monitoring, Diagnosis, and Prognostics for Power Equipment

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 March 2026 | Viewed by 2881

Special Issue Editors

Dr. Zhan Meng

E-Mail Website
Guest Editor
Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada
Interests: advanced metering infrastructure; state estimation for power equipment; microgrid; data analytics
Dr. Cheng He

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: intelligent computing; power equipment condition monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Chuanji Zhang

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: condition monitoring; electrical measurement; online monitoring; power system; data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Zhu Zhang

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230002, China
Interests: condition monitoring; electrical measurement; power system; data-driven methods

Special Issue Information

Dear Colleagues,

Condition monitoring, fault diagnosis, and prognostics have emerged as critical research areas in modern power systems. As global energy demands grow, the reliability, efficiency, and safety of power equipment become paramount. Failures in key components such as transformers, circuit breakers, generators, and renewable energy resources can lead to significant downtime, costly repairs, and disruptions in energy supply.

Advances in sensor technology, artificial intelligence (AI), the Internet of Things (IoT), and data analytics have revolutionized the ability to monitor the health of equipment, predict potential failures, enable real-time decision making, and implement predictive maintenance strategies in addition to conventional periodic maintenance. These innovations are pivotal for transitioning to smarter, more resilient power systems, especially in the context of renewable energy integration and grid modernization.

This Special Issue seeks to provide a comprehensive platform to explore the latest theoretical advancements and practical applications in power equipment management, enhancing the operational efficiency and resilience of power systems.

The research topic may include (but not limited to) the following:

  • Sensor technologies for real-time monitoring;
  • Signal processing techniques for diagnostic purposes;
  • AI and machine learning applications in fault identification and prognostics;
  • Power equipment maintenance strategies;
  • IoT for power equipment condition monitoring;
  • Advanced monitoring and diagnosis tool development;
  • Augmented and virtual reality tools for equipment inspection;
  • Simulation tools for predicting complex failure modes;
  • Real-world implementations of monitoring and prognostics;
  • Lessons learned from power equipment maintenance;
  • Economic and operational benefits of predictive strategies;
  • Development of standards for monitoring and diagnostics.

This Special Issue invites researchers, practitioners, and experts from academia, utilities, and industry to contribute original research, reviews, real-world power equipment maintenance expertise, and innovative product development that address the related topics. We look forward to receiving your valuable work.

Dr. Zhan Meng
Dr. Cheng He
Dr. Chuanji Zhang
Dr. Zhu Zhang
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. Electronics 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 2400 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

  • power equipment diagnostics
  • condition monitoring
  • asset management
  • state estimation
  • fault prognostics
  • predictive maintenance
  • electrical equipment reliability

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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

21 pages, 4215 KB  
Article
Lifetime Prediction of SiC MOSFET by LSTM Based on IGWO Algorithm
by Peng Dai, Junyi Bao, Zheng Gong, Mingchang Gao and Qing Xu
Electronics 2025, 14(22), 4486; https://doi.org/10.3390/electronics14224486 - 17 Nov 2025
Viewed by 285
Abstract
SiC MOSFETs face prominent reliability issues due to higher voltage resistance requirements and continued device miniaturization. The lifetime prediction of SiC MOSFET plays a crucial role in improving the reliability of devices and systems. However, existing methods still face challenges in terms of [...] Read more.
SiC MOSFETs face prominent reliability issues due to higher voltage resistance requirements and continued device miniaturization. The lifetime prediction of SiC MOSFET plays a crucial role in improving the reliability of devices and systems. However, existing methods still face challenges in terms of adaptability, stability, and accuracy due to the complexity of the failure process in SiC MOSFET. This article proposes an improved grey wolf optimizer-based long short-term memory (IGWO-LSTM) model for SiC MOSFET lifetime prediction. The model introduces a Tent chaotic mapping to generate an initial population with optimal distribution, ensuring comprehensive search space coverage and enhancing dynamic search adaptability. Then, a nonlinear control parameter strategy and the principle of particle swarm optimization (PSO) are added. The feature extraction capability of the model is strengthened, and the exploration and exploitation phases are dynamically balanced. The optimizations enable faster discovery of the global optimum while maintaining solution quality, thereby improving prediction accuracy and stability. Finally, power cycling experiments were conducted on two types of SiC MOSFETs with different internal resistances to validate the effectiveness of the proposed model. The proposed IGWO-LSTM model achieves high prediction accuracy, with R2 values of 96.2%, 94.8%, 94.1%, and 93.9% for four SiC MOSFETs, and RMSE values as low as 0.0117, 0.0143, 0.0152, and 0.0158, respectively. This represents an average improvement in R2 by 16%, 8%, and 4%, and a reduction in RMSE by up to 67.03%, 50.39%, and 31.57% compared with other intelligent models. Similarly, IGWO-LSTM achieves reductions in MAE of approximately 68%, 50%, and 30%, with corresponding reductions in MAPE of about 70%, 48%, and 26%, respectively. The results demonstrate superior performance in prediction accuracy, stability, and adaptability of the proposed model. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring, Diagnosis, and Prognostics for Power Equipment)
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17 pages, 1904 KB  
Article
Optimal Deployment of Low-Voltage Instrument Transformers Considering Time-Varying Risk Assessment
by Yinglong Diao, Jiawei Fan, Kangmin Hu, Lei Yang and Qiang Yao
Electronics 2025, 14(22), 4361; https://doi.org/10.3390/electronics14224361 - 7 Nov 2025
Viewed by 237
Abstract
To address the “metering blind zone” problem in distribution networks caused by flood disasters, this paper proposes an optimal deployment strategy for low-voltage instrument transformers (LVITs) based on time-varying risk assessment. A comprehensive model quantifying real-time node importance during disaster progression is established, [...] Read more.
To address the “metering blind zone” problem in distribution networks caused by flood disasters, this paper proposes an optimal deployment strategy for low-voltage instrument transformers (LVITs) based on time-varying risk assessment. A comprehensive model quantifying real-time node importance during disaster progression is established, considering cascading faults and dynamic load fluctuations. A multi-objective optimization model minimizes deployment costs while maximizing fault coverage, incorporating dynamic response constraints. A Genetic-Greedy Hybrid Algorithm (GGHA) with intelligent initialization and elite retention mechanisms is proposed to solve the complex spatiotemporal coupling problem. Simulation results demonstrate that GGHA achieves solution quality of 0.847, outperforming PSO, GA, and GD by 7.5%, 11.7%, and 8.7%, respectively, with convergence stability within ±2.5%. The strategy maintains 100% normal coverage and 73.3–95.5% disaster coverage across flood severity levels, exhibiting strong feasibility and generalizability on IEEE 123-node and 33-node test systems. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring, Diagnosis, and Prognostics for Power Equipment)
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16 pages, 3663 KB  
Article
MSRDSN: A Novel Deep Learning Model for Fault Diagnosis of High-Voltage Disconnectors
by Shijian Zhu, Peilong Chen, Xin Li, Qichen Deng, Yuxiang Liao and Jiangjun Ruan
Electronics 2025, 14(21), 4151; https://doi.org/10.3390/electronics14214151 - 23 Oct 2025
Viewed by 418
Abstract
The operational state of high-voltage disconnectors plays a critical role in ensuring the safety, stability, and power supply reliability of electrical systems. To enable accurate identification of the operational status of high-voltage disconnectors, this paper proposes a fault diagnosis method based on a [...] Read more.
The operational state of high-voltage disconnectors plays a critical role in ensuring the safety, stability, and power supply reliability of electrical systems. To enable accurate identification of the operational status of high-voltage disconnectors, this paper proposes a fault diagnosis method based on a Multi-Scale Residual Depthwise Separable Convolutional Neural Network (MSRDSN). First, wavelet transform is applied to vibration signals to perform multi-scale analysis and enhance detail resolution. Then, a novel network architecture, referred to as RDSN, is constructed to extract discriminative high-level features from vibration signals by integrating residual learning blocks and depthwise separable convolution blocks. Furthermore, a combined loss function is introduced to optimize the RDSN, which simultaneously maximizes inter-class distance, minimizes intra-class distance, and reduces feature redundancy. Experimental results show that the proposed method achieves a top accuracy of 99.44% on a balanced dataset, outperforming the sub-optimal approach by 1.11%. This study offers a novel and effective solution for fault diagnosis in high-voltage disconnectors. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring, Diagnosis, and Prognostics for Power Equipment)
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19 pages, 2795 KB  
Article
State Analysis of Grouped Smart Meters Driven by Interpretable Random Forest
by Zhongdong Wang, Zhengbo Zhang, Weijiang Wu, Zhen Zhang, Xiaolin Xu and Hongbin Li
Electronics 2025, 14(15), 3105; https://doi.org/10.3390/electronics14153105 - 4 Aug 2025
Viewed by 526
Abstract
Accurate evaluation of the operational status of smart meters, as the critical interface between the power grid and its users, is essential for ensuring fairness in power transactions. This highlights the importance of implementing rotation management practices based on meter status. However, the [...] Read more.
Accurate evaluation of the operational status of smart meters, as the critical interface between the power grid and its users, is essential for ensuring fairness in power transactions. This highlights the importance of implementing rotation management practices based on meter status. However, the traditional expiration-based rotation method has become inadequate due to the extended service life of modern smart meters, necessitating a shift toward status-driven targeted management. Existing multifactor comprehensive assessment methods often face challenges in balancing accuracy and interpretability. To address these limitations, this study proposes a novel method for analyzing the status of smart meter groups using an interpretable random forest model. The approach incorporates an expert-knowledge-guided grouping assessment strategy, develops a multi-source heterogeneous feature set with strong correlations to meter status, and enhances the random forest model with the SHAP (SHapley Additive exPlanations) interpretability framework. Compared to conventional methods, the proposed approach demonstrates superior efficiency and reliability in predicting the failure rates of smart meter groups within distribution network areas, offering robust support for the maintenance and management of smart meters. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring, Diagnosis, and Prognostics for Power Equipment)
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17 pages, 4372 KB  
Article
Research of 110 kV High-Voltage Measurement Method Based on Rydberg Atoms
by Yinglong Diao, Zhaoyang Qu, Nan Qu, Jie Cao, Xinkun Li, Xiaoyu Xu and Shuhang You
Electronics 2025, 14(15), 2932; https://doi.org/10.3390/electronics14152932 - 23 Jul 2025
Viewed by 709
Abstract
Accurate measurement of high voltages is required to guarantee the safe and stable operation of power systems. Modern power systems, which are mainly based on new energy sources, require high-voltage measurement instruments and equipment with characteristics such as high accuracy, wide frequency bandwidth, [...] Read more.
Accurate measurement of high voltages is required to guarantee the safe and stable operation of power systems. Modern power systems, which are mainly based on new energy sources, require high-voltage measurement instruments and equipment with characteristics such as high accuracy, wide frequency bandwidth, broad operating ranges, and ease of operation and maintenance. However, it is difficult for traditional electromagnetic measurement transformers to meet these requirements. To address the limitations of conventional Rydberg atomic measurement methods in low-frequency applications, this paper proposes an enhanced Rydberg measurement approach featuring high sensitivity and strong traceability, thereby enabling the application of Rydberg-based measurement methodologies under power frequency conditions. In this paper, a 110 kV high-voltage measurement method based on Rydberg atoms is studied. A power-frequency electric field measurement device is designed using Rydberg atoms, and its internal electric field distribution is analyzed. Additionally, a decoupling method is proposed to facilitate voltage measurements under multi-phase overhead lines in field conditions. The feasibility of the proposed method is confirmed, providing support for the future development of practical measurement devices. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring, Diagnosis, and Prognostics for Power Equipment)
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