IEEE Transactions on Dielectrics and Electrical Insulation最新文献

{"title":"Shadow Removal Hyperspectral Imaging for Outdoor Aging Evaluation of Composite Insulators","authors":"Yuan Ou;Yujun Guo;Yihan Fan;Haoran Xiao;Song Xiao;Xueqin Zhang;Guangning Wu","doi":"10.1109/TDEI.2025.3595522","DOIUrl":"https://doi.org/10.1109/TDEI.2025.3595522","url":null,"abstract":"Composite insulators are widely used in power grids, and their aging with increased operational years is a key factor contributing to insulator flashovers and power outages. Accurately detecting the aging state of insulators is crucial for the stability of the power grid. Hyperspectral technology has been preliminarily applied to the state detection of insulators. However, under natural sunlight in actual operating environments, the shading caused by occlusion of insulator sheds results in a significant reduction in detection accuracy. This has become the major obstacle in applying hyperspectral imaging (HSI) for field applications of insulator aging levels. Therefore, this study proposes a shadow removal model based on improved color-lines (ICLs) for ultraviolet (UV) aging assessment of composite insulators under natural light. First, accelerated UV-aged samples were prepared to extract spectral signatures from various aging levels, training a robust support vector machine (SVM) classifier. Next, the background pixels in the hyperspectral images of insulators are removed based on spectral differences between different materials. The shadow regions are located using color space conversion methods, followed by the progressive removal of both umbra and penumbra areas. Combined with a brightness map smoothed by weighted least squares (WLSs), the shadow-free hyperspectral images are generated. Finally, the trained model is used to classify the aging levels of the shadow-restored insulators, with the overall accuracy (OA) improving from 75.00% to 94.44%, outperforming existing shadow recovery algorithms. This method is of significant importance for the field detection of insulator aging levels.","PeriodicalId":13247,"journal":{"name":"IEEE Transactions on Dielectrics and Electrical Insulation","volume":"33 2","pages":"1459-1468"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Small-Sample Partial Discharge Diagnosis for GIS Based on Improved Capsule Generative Adversarial Network and Dual-Mode Generator Optimization","authors":"Qianzhen Jing;Jing Yan;Yanxin Wang;Yingsan Geng;Jianhua Wang","doi":"10.1109/TDEI.2026.3663347","DOIUrl":"https://doi.org/10.1109/TDEI.2026.3663347","url":null,"abstract":"Data-driven deep learning (DL) methods excel in gas-insulated switchgear (GIS) partial discharge (PD) diagnosis, but their performance falters in data-scarce scenarios. Constrained by the intricate operating environment of GIS and the transient and stochastic nature of PD, acquiring sufficient PD samples in practical applications remains challenging. Therefore, this article proposes an improved capsule generative adversarial network (GAN) for small-sample GIS PD diagnosis. First, a capsule network (CN) is introduced in both the generator and the discriminator to extract more comprehensive feature information from PD signals, thus avoiding the potential loss of crucial features due to pooling operations. Then, a dual-mode optimization method is used to update the model parameters of the generator, and sample quality and diversity are used as evaluation indicators to guide the training process of the generator, realizing automatic optimization of generator parameter-level model construction. Meanwhile, the discriminator stabilizes the GAN training by adopting the Wasserstein distance and incorporating a gradient penalty (GP) term. Experimental results show that the proposed method achieves diagnostic accuracy of 97.47% in small datasets, significantly outperforming other methods and exhibiting robust performance, providing a viable solution for high-precision and robust GIS PD diagnosis with limited samples.","PeriodicalId":13247,"journal":{"name":"IEEE Transactions on Dielectrics and Electrical Insulation","volume":"33 2","pages":"1529-1538"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security Region of DC-GIL Insulator Considering Electromechanical Breakdown","authors":"Jianan Dong;Boxue Du;Yifang Wang;Hucheng Liang;Daomin Min","doi":"10.1109/TDEI.2026.3664773","DOIUrl":"https://doi.org/10.1109/TDEI.2026.3664773","url":null,"abstract":"DC-GIL insulators are key components that provide both electrical insulation and mechanical support. However, their insulation and mechanical designs have traditionally been developed separately, without accounting for the influence of mechanical stress on insulation breakdown. This study investigates how mechanical stress affects the breakdown strength of epoxy/Al₂O₃ composite samples at various temperatures. Results show that breakdown strength decreases significantly with increasing mechanical stress, and this effect becomes even more pronounced at elevated temperatures. The security region of DC-GIL insulators can be defined as the region bounded by the breakdown-strength (<inline-formula> <tex-math>${E} _{text {B}}$ </tex-math></inline-formula>) and tensile-strength (<inline-formula> <tex-math>$sigma _{text {B}}$ </tex-math></inline-formula>) intercepts on the coordinate axes. Accordingly, the criterion <inline-formula> <tex-math>$E$ </tex-math></inline-formula>/<inline-formula> <tex-math>${E} _{text {B}} + sigma $ </tex-math></inline-formula>/<inline-formula> <tex-math>$sigma _{text {B}} lt 1$ </tex-math></inline-formula> can be used to assess the safety margin of DC-GIL insulators.","PeriodicalId":13247,"journal":{"name":"IEEE Transactions on Dielectrics and Electrical Insulation","volume":"33 2","pages":"1555-1558"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Extraction and Classification of Partial Discharge Signals in C4F7N-Based Gas-Insulated Systems: A Time-Domain and Machine Learning Approach","authors":"Rahmat Ullah;Alistair Reid;Manu Haddad;Mini Nambiar;Peter Taddei;Matthew Barnett","doi":"10.1109/TDEI.2025.3610575","DOIUrl":"https://doi.org/10.1109/TDEI.2025.3610575","url":null,"abstract":"In the quest for an environmentally friendly alternative to SF₆ gas to reduce carbon emissions, the C₄F₇N gas mixture (C₄F₇N/O₂/CO<inline-formula> <tex-math>${}_{{2}}text {)}$ </tex-math></inline-formula> emerged as a good candidate due to its lower environmental hazards and good dielectric strength. However, these new gas mixtures bring transition-related challenges due to their altered partial discharge (PD) patterns and dynamics, which complicate reliable PD detection and classification in C₄F₇N-insulated systems. Manufacturing or assembly flaws in gas-insulated equipment (GIE) can enhance local electric fields, triggering PDs. Identifying PD sources is critical for effective online monitoring of high-voltage equipment, enabling early detection of insulation degradation and preventing potential failures. Historically, phase-resolved PD patterns have been employed for defect identification. However, challenges arise when multiple PD sources are active simultaneously, as their patterns may partially overlap, leading to misunderstanding in the identification process. In this research, a machine learning algorithm is developed to classify PD sources using statistical and probabilistic analysis of time-domain parameters of PD pulses. Several PD source topologies are designed, including protrusion, metallic particles, and floating electrode arrangements. Various time-domain parameters of PD pulses, along with their peak amplitude, are measured. The model employs Weibull distribution analysis, statistical variables such as kurtosis and skewness, and machine learning approaches to effectively distinguish between various types of PDs. Achieving approximately 98% accuracy demonstrates the model’s effectiveness in classifying simultaneous PD sources. Moreover, the study confirms that time-domain statistical feature analysis significantly improves the reliability and safety of electrical systems using C₄F₇N gas mixtures.","PeriodicalId":13247,"journal":{"name":"IEEE Transactions on Dielectrics and Electrical Insulation","volume":"33 2","pages":"1511-1518"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partial Discharge Evolution of Hairpin Winding Insulation During Multistress Aging Under Various Square Wave Voltages and Environmental Conditions","authors":"Taoran Yang;Yi Cai;Yalin Wang;Gang Zheng;Yizhong Zhang;Yi Yin","doi":"10.1109/TDEI.2025.3610824","DOIUrl":"https://doi.org/10.1109/TDEI.2025.3610824","url":null,"abstract":"With the rapid development of wide bandgap devices and electric vehicles (EVs), electric machines with hairpin windings have gained extensive attention due to the superior slot-filling factor and relatively higher power density. As EVs face a harsher environment, such as high elevation where air pressure is relatively low, the hairpin winding insulation, subjected to multistresses including high d<italic>V</i>/d<italic>t</i> square wave voltages, extreme temperatures, and low air pressures, could trigger partial discharge (PD) that may compromise insulation. However, critical issues such as the PD evolution mechanism under diverse operational conditions and the fundamental mechanism of insulation failure have not been systematically investigated yet. This article focuses on PD evolution during multistress aging of hairpin winding, which could be affected by PD characteristics. A down-mixing ultrahigh-frequency (UHF) PD detection method is applied. PD characteristics such as PD inception voltage (PDIV) and phase-resolved PD (PRPD) are analyzed under square wave voltages with various rise times and frequencies at different temperatures and air pressures. Accelerated aging experiments are performed and a lifetime model is proposed considering the effect of square wave voltage parameters and environmental conditions. With the decrease in air pressure and square wave voltage rising time, and the increase in temperature and square wave frequency, the lifetime decreases. The aging process comprises a development stage, characterized by gradually decreased PD magnitudes, and a pre-breakdown stage, which appears near the end of the lifetime and displays a slight increase in PD magnitude.","PeriodicalId":13247,"journal":{"name":"IEEE Transactions on Dielectrics and Electrical Insulation","volume":"33 2","pages":"1490-1500"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical Simulation and Analysis of Stress Distribution in Basin-Type Insulator Under Multiphysics Coupling","authors":"Kang Zhaoyang;Wu Hongbin;Hou Kaining;Yang Guangdi;Zhu Ran;Ren Fuqiang;Zhang Hongru;Li Qingquan;Liu Hongshun;He Dongxin;Wang Peijin","doi":"10.1109/TDEI.2025.3623571","DOIUrl":"https://doi.org/10.1109/TDEI.2025.3623571","url":null,"abstract":"The basin-type insulator, a critical component of gas-insulated transmission lines (GILs), significantly influences system reliability through its stress distribution. This study develops a coupled electromagnetic–temperature–fluid–stress field model for a 550-kV GIL basin-type insulator to investigate its stress distribution under operational conditions. We measure the thermal conductivity (<inline-formula> <tex-math>${K}text {)}$ </tex-math></inline-formula>, heat capacity (<inline-formula> <tex-math>${C}_{text {p}}text {)}$ </tex-math></inline-formula>, elastic modulus (<inline-formula> <tex-math>${E}text {)}$ </tex-math></inline-formula>, Poisson’s ratio (<inline-formula> <tex-math>$nu text {)}$ </tex-math></inline-formula>, and thermal expansion coefficient (<inline-formula> <tex-math>$alpha text {)}$ </tex-math></inline-formula> of epoxy resin from 293.15 to 423.15 K, quantifying their temperature dependence for numerical modeling. A stepwise coupling strategy is proposed, first resolving bidirectional coupling among the electromagnetic field, temperature field, and fluid field to determine temperature distribution, then evaluating the stress field response. Innovatively, the penalty function method and Mooney–Rivlin model are introduced to simulate the contact characteristics of the semiconductive rubber buffer layer. Simulation results reveal that a temperature gradient (maximum difference of 15 K) drives thermal stress, with a maximum von Mises stress of 43.16 MPa, significantly higher than 2.06 MPa from the static model. Stress concentration occurs at the triple-point and concave surface, with the concave surface exhibiting higher stress than the convex surface (difference of 5.13 MPa). The buffer layer reduces interfacial Tresca stress to 5.39 MPa, though local strain remains elevated. These findings provide a theoretical foundation for geometric optimization and reliability enhancement of the insulator.","PeriodicalId":13247,"journal":{"name":"IEEE Transactions on Dielectrics and Electrical Insulation","volume":"33 2","pages":"1469-1479"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anomaly Detection of Partial Discharge Signals in Transformers Based on Self-Supervised Temporal Contrastive Learning","authors":"Ang Li;Guangze Wei;Chunyan Zhang;Jianlei Zhang","doi":"10.1109/TDEI.2025.3595516","DOIUrl":"https://doi.org/10.1109/TDEI.2025.3595516","url":null,"abstract":"Partial discharge (PD) identification in transformers is crucial for ensuring operational safety through timely fault detection. This study introduces the self-supervised temporal anomaly detection via contrastive learning (STAD-CL) model to detect temporal anomalies in time-series sensor data. The approach employs an end-to-end framework for feature extraction, utilizing wavelet transform (WT) convolution-1D (WTConv-1D) to capture multifrequency features by combining convolutional operations with WT. Additionally, dilated convolution-1D (DConv-1D) is introduced to extract temporal features across varying scales via dilated convolution. The temporal slicing strategy targets local features within the signal, thereby reducing redundancy. And the positive sample selection learns position-invariant representations from overlapping regions, effectively mitigating representation collapse. A novel dual-level contrastive loss, incorporating instance-level and temporal-level contrasts with an attention mechanism, further enhances learning performance. Finally, an anomaly detection criterion is established, effectively identifying abnormal signals by combining segment-wise signal scores with predefined thresholds. Experimental results demonstrate that STAD-CL achieves state-of-the-art results on benchmark datasets for transformer PD monitoring.","PeriodicalId":13247,"journal":{"name":"IEEE Transactions on Dielectrics and Electrical Insulation","volume":"33 2","pages":"1501-1510"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Transactions on Dielectrics and Electrical Insulation Information for Authors","authors":"","doi":"10.1109/TDEI.2026.3675838","DOIUrl":"https://doi.org/10.1109/TDEI.2026.3675838","url":null,"abstract":"","PeriodicalId":13247,"journal":{"name":"IEEE Transactions on Dielectrics and Electrical Insulation","volume":"33 2","pages":"C4-C4"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11460313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Dielectrics and Electrical Insulation Society Information","authors":"","doi":"10.1109/TDEI.2026.3675836","DOIUrl":"https://doi.org/10.1109/TDEI.2026.3675836","url":null,"abstract":"","PeriodicalId":13247,"journal":{"name":"IEEE Transactions on Dielectrics and Electrical Insulation","volume":"33 2","pages":"C3-C3"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11460245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigations on Initial Self-Healing Failure Probability of AC Metallized Film Capacitors","authors":"Zijian Wang;Zichen Wang;Fei Yan;Lin Yang;Yanfeng Ma","doi":"10.1109/TDEI.2025.3610498","DOIUrl":"https://doi.org/10.1109/TDEI.2025.3610498","url":null,"abstract":"Self-healing failure in metallized film capacitors (MFCs) is adverse to their lifetime and will affect the stability of systems. The probability of initial self-healing failure of MFCs is influenced by various factors. In this article, an experimental setup is established to obtain the initial self-healing failure probability of MFCs under ac voltage, and the effects of interlayer pressure, temperature, film thickness, and voltage on the initial self-healing failure probability are studied. The results reveal that, with the interlayer pressure increasing from 200 to 900 kPa, the probability of initial self-healing failure decreases first and then increases, reaching the minimum value at 600 kPa. With the temperature increasing from <inline-formula> <tex-math>$20~^{circ }$ </tex-math></inline-formula>C to <inline-formula> <tex-math>$70~^{circ }$ </tex-math></inline-formula>C, the probability of initial self-healing failure decreases first and then increases, reaching the minimum value at <inline-formula> <tex-math>$50~^{circ }$ </tex-math></inline-formula>C. The probability of initial self-healing failure is negatively correlated with the thickness of the film within the range of 7–<inline-formula> <tex-math>$10~mu $ </tex-math></inline-formula>m. The probability of initial self-healing failure is positively correlated with voltage within the range of 1150–1350 V. In the design and manufacture of MFCs, to reduce the initial self-healing failure probability, steps should be taken to improve interlayer pressure for the outer layer of cylindrical capacitor elements to 600 kPa, such as increasing the turns of wrapped film outside and optimizing heat treatment processes. For flat elements, applying a pressure of 600 kPa is appropriate, measures should be taken to control the internal temperature of MFCs close to <inline-formula> <tex-math>$50~^{circ }$ </tex-math></inline-formula>C, and a thicker film can reduce the probability of initial self-healing failure. Under the present design field strength, the initial self-healing failure probability of the MFCs is relatively low, and the design field strength is reasonable. The research results can provide a reference for the design of MFCs.","PeriodicalId":13247,"journal":{"name":"IEEE Transactions on Dielectrics and Electrical Insulation","volume":"33 2","pages":"1547-1554"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}