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Cutting-Edge Insights into Electrical Equipment Lifespan Assessment

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (15 January 2026) | Viewed by 1782

Special Issue Editor


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Guest Editor
School of Electrical Engineering, Chongqing University, Chongqing, China
Interests: high voltage; insulation; non-destructive testing; transformer
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Special Issue Information

Dear Colleagues,

Electrical equipment serves as the physical foundation of energy systems, and ensuring its safety is crucial for maintaining reliable energy transmission worldwide. However, due to various factors such as design flaws, material defects, operational environment fluctuations, and particularly long-term aging, the actual service life of equipment may fall short of initial expectations. Therefore, accurately assessing the operational lifespan of equipment based on its long-term performance is of paramount significance for ensuring operational safety. 

To achieve precise lifespan prediction and evaluation, numerous scholars have conducted extensive research from diverse perspectives, including mechanistic modeling, detection methods, and data processing techniques. Accordingly, this Special Issue aims to showcase recent advancements in the field of assessing the lifespan of electrical equipment. Topics of interest include, but are not limited to, the following: 

  1. Research on the failure mechanisms of electrical equipment;
  2. Novel sensing methodologies for online monitoring of electrical equipment;
  3. Data processing techniques for lifespan prediction and condition assessment, particularly AI-integrated algorithms;
  4. Other technological approaches contributing to accurate lifespan evaluation and prediction.

Prof. Dr. Li Cheng
Guest Editor

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

  • failure mechanisms
  • electrical equipment
  • lifespan prediction

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Published Papers (2 papers)

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Research

17 pages, 2267 KB  
Article
Research on Microwave Non-Destructive Testing Method for Defects in 10 kV Distribution Cable Intermediate Joints
by Wangjun Deng, Li Cheng, Xiying Wang, Hao Luo and Tengyi Zhang
Energies 2026, 19(2), 499; https://doi.org/10.3390/en19020499 - 19 Jan 2026
Viewed by 513
Abstract
This study aims to propose a defect diagnosis method for distribution cable intermediate joints based on microwave reflection. The research focuses on 10 kV cold-shrink-type distribution cable intermediate joints, employing both simulation analysis and experimental methods. Firstly, a microwave defect detection model for [...] Read more.
This study aims to propose a defect diagnosis method for distribution cable intermediate joints based on microwave reflection. The research focuses on 10 kV cold-shrink-type distribution cable intermediate joints, employing both simulation analysis and experimental methods. Firstly, a microwave defect detection model for intermediate joints is derived. CST simulations are conducted to analyze the variation of the reflection coefficient (S11) under different detection frequencies, defect depths, and defect types. Next, flat plate and real prototype samples of intermediate joints with defects such as insulation scratches, conductive impurities, and moisture ingress are fabricated. A microwave reflection detection platform is established to test the artificially defective samples. Reflection voltage signals corresponding to different defects are obtained. The concept of the relative value of the reflection voltage difference is then introduced, resulting in significant changes in the detection results, which effectively indicate the presence of different defects. Finally, the reflection voltage signals under different defect sizes, silicone rubber thicknesses, detection distances, and detection angles are studied. The results show that this method is capable of detecting defects as small as 2 mm in width and 0.2 mm in depth. The silicone rubber thickness, detection distance, and detection angle significantly affect the detection results. This demonstrates that microwave reflection signals can effectively identify the type and severity of defects within cable intermediate joints, and the method can be extended to detect internal defects in other layered composite insulation structures. Full article
(This article belongs to the Special Issue Cutting-Edge Insights into Electrical Equipment Lifespan Assessment)
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13 pages, 2669 KB  
Article
Moisture Condition Assessment of Oil–Paper Insulation Based on Frequency-Domain Dielectric Spectroscopy and Relaxation Time Distribution
by Yanpeng Hao, Yibo Yang, Zikui Shen, Hanhui Xiang, Ningfeng Zhou, Haoyi Zhang and Jun Deng
Energies 2025, 18(24), 6436; https://doi.org/10.3390/en18246436 - 9 Dec 2025
Cited by 1 | Viewed by 731
Abstract
The Debye model and its modified forms are widely applied to interpret relaxation polarization processes in dielectric media. However, these models generally require prior assumptions regarding the number of polarization branches, which makes it difficult to construct an equivalent circuit model that accurately [...] Read more.
The Debye model and its modified forms are widely applied to interpret relaxation polarization processes in dielectric media. However, these models generally require prior assumptions regarding the number of polarization branches, which makes it difficult to construct an equivalent circuit model that accurately reflects the real relaxation characteristics. In this work, frequency-domain dielectric spectra of oil–paper insulation samples with different moisture contents were measured and analyzed. A distributed Debye model considering the probabilistic distribution of relaxation branches was established, and the corresponding relaxation time distribution was obtained using discretization and regularization techniques. Based on this, an accurate moisture condition assessment method for oil–paper-insulated bushings is proposed. The results indicate that, with increasing moisture content, both the real and imaginary parts of the complex capacitance increase significantly in the low-frequency region, while remaining nearly constant at high frequencies. Meanwhile, the peak of the imaginary part shifts toward higher frequencies. By applying the proposed discretization and regularization method to the measured dielectric spectra, the relaxation time distribution function can be accurately derived. As the moisture content increases, the relaxation time constant corresponding to the third relaxation peak gradually decreases. The constructed assessment model exhibits high accuracy, with a maximum error below 2.66%. Full article
(This article belongs to the Special Issue Cutting-Edge Insights into Electrical Equipment Lifespan Assessment)
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