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Insulating Materials for Future Power Systems: Performance Analysis, Defect Detection...
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Insulating Materials for Future Power Systems: Performance Analysis, Defect Detection and Condition Assessment

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D1: Advanced Energy Materials".

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 8775

Special Issue Editors

Dr. Xianhao Fan

E-Mail Website
Guest Editor
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Interests: dielectric insulation; insulation aging; intelligent optimization; state evaluation; fault diagnosis
Dr. Chuangyang Li

E-Mail Website
Guest Editor
Department of Electrical Engineering, State Key Laboratory of Power System, Tsinghua University, Beijing 100084, China
Interests: insulating material; DC gas-insulated equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Fangwei Liang

E-Mail Website
Guest Editor
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Interests: high voltage engineering; power transmission; partial discharge; dielectric insulation
Dr. Jiefeng Liu

E-Mail Website
Guest Editor
Department of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: fault diagnosis and condition monitoring of high-voltage power equipment
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weidong Liu

E-Mail Website
Guest Editor
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Interests: Interrupting performance of SF6 circuit breakers; Measurement, analysis and suppression of switching transients in power equipment and networks; on-line condition monitoring of power equipment

Special Issue Information

Dear Colleagues,

A wide variety of electrical materials, such as polymeric insulating materials, energy storage materials, functional ceramics, semi-conductive sensing materials, and high-conductive metallic materials, compose the complicated power transmission system. Recently, modern renewable energy systems will replace traditional energy systems owing to the more precise and controlled power stations. In this context, the insulation materials, as one of the core components in electrical power equipment, will undergo unprecedented challenges and opportunities that may enhance the operational complexity and reduces the power system’s safety and reliability.

The variations in operational conditions mark a question on the insulation strength of newly developed countless ultra-high voltage (UHV) assets. Consequently, there is an essential need for a credible performance analysis, defect detection, and condition assessment of insulation materials in UHV equipment.

Therefore, in this Research Topic, we call for papers focusing on the fabrication, performance analysis, deterioration mechanism, defect detection, and condition assessment of new electrical materials. We welcome researchers to contribute Original Research, Brief Research Reports, and Review papers for this Research Topic. Potential topics include, but are not limited to:

  • Advanced measurement and characterization technology
  • Advanced electrical materials fabrication and their deterioration mechanism
  • Advanced insulation defect detection and condition assessment technique
  • Dielectric materials and relaxation phenomena
  • Operation, maintenance, and optimal design of UHV equipment
  • Other related aspects

Dr. Xianhao Fan
Dr. Chuangyang Li
Dr. Fangwei Liang
Dr. Jiefeng Liu
Prof. Dr. Weidong Liu
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

  • high voltage engineering
  • insulation materials
  • performance characterization and analysis
  • defect detection
  • condition assessment

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Published Papers (6 papers)

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Research

10 pages, 2715 KiB  
Article
Optical Detection and Cluster Analysis of Metal-Particle-Triggered Alternating Current Optical Partial Discharge in SF6
by Hanhua Luo, Yan Liu, Chong Guo and Zuodong Liang
Energies 2025, 18(7), 1649; https://doi.org/10.3390/en18071649 - 26 Mar 2025
Viewed by 163
Abstract
Accurately detecting defect-induced photon emissions enables early defect detection and characterization. To address this, a defect evolution state recognition model based on phase-resolved photon counting and dimensionality reduction calculations is proposed under alternating current (AC) excitation. Initially, photon information from protruding metal defects [...] Read more.
Accurately detecting defect-induced photon emissions enables early defect detection and characterization. To address this, a defect evolution state recognition model based on phase-resolved photon counting and dimensionality reduction calculations is proposed under alternating current (AC) excitation. Initially, photon information from protruding metal defects simulated using needle–plane electrodes during partial discharge (PD) evolution is analyzed in SF6. Subsequently, phase-resolved photon counting (PRPC) techniques and statistical analysis are employed to extract feature parameters for quantitative characterization of defect-induced photon responses. Finally, a t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction analysis is utilized to establish criteria for categorizing defect evolution states. The findings reveal that metal-particle-triggered optical PRPC maintains the obvious polarity effect, and the entire evolution of the discharge can be divided into three processes. These research findings are expected to advance the accurate assessment of operational risks in gas-insulated systems. Full article
(This article belongs to the Special Issue Insulating Materials for Future Power Systems: Performance Analysis, Defect Detection and Condition Assessment)
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16 pages, 3803 KiB  
Article
Optimization of Fault Current Limiter Reactance Based on Joint Simulation and Penalty Function-Constrained Algorithm
by Jun Zhao, Chao Xing, Zhigang Zhang, Boyuan Liang, Lu Sun, Bin Wei, Weiqi Qin and Shuguo Gao
Energies 2025, 18(5), 1077; https://doi.org/10.3390/en18051077 - 23 Feb 2025
Viewed by 366
Abstract
This paper proposes a novel optimization method for fault current limiter (FCL) reactance configuration based on joint simulation and penalty function constraint optimization. By integrating MATLAB and ATP for joint simulation, the method accurately derives the constraint conditions of the objective optimization function, [...] Read more.
This paper proposes a novel optimization method for fault current limiter (FCL) reactance configuration based on joint simulation and penalty function constraint optimization. By integrating MATLAB and ATP for joint simulation, the method accurately derives the constraint conditions of the objective optimization function, providing critical data support for the optimization process. To address the challenges of high computational complexity and solution difficulties in constrained optimization, the Penalty Function Method (PFM) is employed to transform the original constrained optimization problem into a standard unconstrained optimization problem, significantly reducing computational complexity and ensuring the feasibility of the solution. On this basis, the Gravitational Search Algorithm (GSA) is applied to compute the optimal reactance value. Through comparative analysis of engineering case studies, the superiority of the GSA over the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in optimization performance is validated, further confirming the accuracy and efficiency of the proposed method. The results indicate that this method not only achieves precise calculation results but also significantly improves computational efficiency. Moreover, the integration of PFM and GSA demonstrates excellent robustness, providing reliable technical support for the optimized deployment of fast-switching fault current limiters in large-scale power grids. Full article
(This article belongs to the Special Issue Insulating Materials for Future Power Systems: Performance Analysis, Defect Detection and Condition Assessment)
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13 pages, 5802 KiB  
Article
Fault Diagnosis for Motor Bearings via an Intelligent Strategy Combined with Signal Reconstruction and Deep Learning
by Weiguo Li, Naiyuan Fan, Xiang Peng, Changhong Zhang, Mingyang Li, Xu Yang and Lijuan Ma
Energies 2024, 17(19), 4773; https://doi.org/10.3390/en17194773 - 24 Sep 2024
Cited by 3 | Viewed by 813
Abstract
To overcome the incomplete decomposition of vibration signals in traditional motor-bearing fault diagnosis algorithms and improve the ability to characterize fault characteristics and anti-interference, a diagnostic strategy combining dual signal reconstruction and deep learning architecture is proposed. In this study, an improved complete [...] Read more.
To overcome the incomplete decomposition of vibration signals in traditional motor-bearing fault diagnosis algorithms and improve the ability to characterize fault characteristics and anti-interference, a diagnostic strategy combining dual signal reconstruction and deep learning architecture is proposed. In this study, an improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD)-based signal reconstruction method is first introduced to extract features representing motor bearing faults. A feature matrix construction method based on improved information entropy is then proposed to quantify these fault features. Finally, a fault diagnosis algorithm architecture integrating a multi-scale convolutional neural network (MSCNN) with attention mechanisms and a bidirectional long short-term memory network (BiLSTM) is developed. The experimental results for four fault states show that this model can effectively extract fault features from original vibration signals and, compared to other fault diagnosis models, offer high diagnostic accuracy and strong generalization, maintaining high accuracy even under varying speeds and noise interference. Full article
(This article belongs to the Special Issue Insulating Materials for Future Power Systems: Performance Analysis, Defect Detection and Condition Assessment)
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21 pages, 14914 KiB  
Article
Composite Insulator Defect Identification Method Based on Acoustic–Electric Feature Fusion and MMSAE Network
by Bizhen Zhang, Shengwen Shu, Cheng Chen, Xiaojie Wang, Jun Xu and Chaoying Fang
Energies 2023, 16(13), 4906; https://doi.org/10.3390/en16134906 - 23 Jun 2023
Cited by 2 | Viewed by 1659
Abstract
Aiming to solve the partial discharge problem caused by defects in composite insulators, most existing live detection methods are limited by the subjectivity of human judgment, the difficulty of effective quantification, and the use of a single detection method. Therefore, a composite insulator [...] Read more.
Aiming to solve the partial discharge problem caused by defects in composite insulators, most existing live detection methods are limited by the subjectivity of human judgment, the difficulty of effective quantification, and the use of a single detection method. Therefore, a composite insulator defect diagnosis model based on acoustic–electric feature fusion and a multi-scale perception multi-input of stacked auto-encoder (MMSAE) network is proposed in this paper. Initially, during the withstanding voltage experiment, the electromagnetic wave spectrometer and ultrasonic detector were used to collect and process the data of six types of composite insulator samples with artificial defects. The electromagnetic wave spectrum, ultrasonic power spectral density, and n-S map were then obtained. Then, the network architecture of MMSAE was built by integrating a stacked auto-encoder and multi-scale perception module; the feature extraction and fusion methods of the electromagnetic wave spectrum and ultrasonic signal were investigated. The proposed method was used to diagnose test samples, and the diagnostic results were compared to those obtained using a single input source and the artificial neural network (ANN) method. The results demonstrate that the detection accuracy of acoustic–electric feature fusion is greater than that of a single feature; the accuracy of the proposed method is 99.17%, which is significantly higher than the accuracy of the conventional ANN method. Finally, composite insulator defect diagnosis software based on PYQT5 and Keras was developed. Ten 500 kV aging composite insulators were used to validate the effectiveness of the proposed method and design software. Full article
(This article belongs to the Special Issue Insulating Materials for Future Power Systems: Performance Analysis, Defect Detection and Condition Assessment)
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13 pages, 5711 KiB  
Article
Comprehensive Properties of Grafted Polypropylene Insulation Materials for AC/DC Distribution Power Cables
by Shangshi Huang, Yuxiao Zhou, Shixun Hu, Hao Yuan, Jun Yuan, Changlong Yang, Jun Hu, Qi Li and Jinliang He
Energies 2023, 16(12), 4701; https://doi.org/10.3390/en16124701 - 14 Jun 2023
Cited by 11 | Viewed by 2521
Abstract
Polypropylene (PP) exhibits excellent insulation properties, high thermo-stability, and recyclable nature, thus expected to be the next-generation insulation material for HV cable application. Chemical grafting modification is an effective technology to improve the electrical properties of polypropylene. In this paper, we develop and [...] Read more.
Polypropylene (PP) exhibits excellent insulation properties, high thermo-stability, and recyclable nature, thus expected to be the next-generation insulation material for HV cable application. Chemical grafting modification is an effective technology to improve the electrical properties of polypropylene. In this paper, we develop and report a new-type grafted PP insulation material by water-phase grafting technology. The comprehensive properties including electrical, thermal, and mechanical of it are tested and compared with traditional cable insulation material—crosslinked polyethylene (XLPE). The results show that the grafted PP holds superior thermal properties and enough mechanical flexibility. The electrical properties are of significant advantages, including resistivity enhanced by nearly two degrees of magnitudes, AC/DC breakdown strength raised by over 20%, and obviously suppressed space charge accumulation. These results indicate that grafted PP is very suitable for application in HV cable systems, either AC or DC. This research lays a foundation for the research and development of the next-generation recyclable polypropylene cables at higher voltage levels. Full article
(This article belongs to the Special Issue Insulating Materials for Future Power Systems: Performance Analysis, Defect Detection and Condition Assessment)
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14 pages, 210757 KiB  
Article
Impact of Air Gap Defects on the Electrical and Mechanical Properties of a 320 kV Direct Current Gas Insulated Transmission Line Spacer
by Yuan Deng, Xianhao Fan, Hanhua Luo, Yao Wang, Keyan Wu, Fangwei Liang and Chuanyang Li
Energies 2023, 16(10), 4006; https://doi.org/10.3390/en16104006 - 10 May 2023
Cited by 5 | Viewed by 1757
Abstract
Air gap defects inside a spacer reduce its insulation performance, resulting in stress concentration, partial discharge, and even flashover. If such gap defects are located at the interface between the insulation and conductor, a decrease in mechanical stress may occur. In this work, [...] Read more.
Air gap defects inside a spacer reduce its insulation performance, resulting in stress concentration, partial discharge, and even flashover. If such gap defects are located at the interface between the insulation and conductor, a decrease in mechanical stress may occur. In this work, a finite element method-based simulation model is developed to analyze the influence of gap defects on the electrical and mechanical properties of a ±320 kV direct current gas insulated line (DC GIL) spacer. Present findings reveal that a radially distributed air gap produces a more significant effect on the electric field distribution, and an electric field strength 1.7 times greater than that of the maximum surface value is observed at the air gap. The axial distribution dominates the distortion of the surface stress by generating a stress concentration region in which the maximum stress of the air gap is twice the pressure in the surrounding area. Full article
(This article belongs to the Special Issue Insulating Materials for Future Power Systems: Performance Analysis, Defect Detection and Condition Assessment)
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