Abstracts:Presents corrections to the paper, Corrections to “On the Frequency Dependence of the PDIV in Twisted Pair Magnet Wire Analogy in Dry Air”.

Abstracts:This article presents a 3D-printed dielectric conical horn cavity (3D-PDCH)-backed antenna, fabricated using additive manufacturing technology. The inner wall of the horn cavity is coated with copper tape as a cost-effective solution to achieve high-gain antenna at the millimeter-wave (mm-Wave) band (26–32 GHz) for 5G applications. The horn cavity is fed by a dielectric resonator antenna (DRA)-based MIMO system, which consists of four orthogonally placed cylindrical DRA circular arrays excited by an aperture-coupled feed line. To enhance isolation, metallic vias are introduced into the dielectric arms of the circular arrays. These vias interact with the electromagnetic fields to control their distribution and improve inter-element isolation (>20 dB). Moreover, the gain is improved by using a low-cost 3D-PDCH, which shapes the phase front of the radiated wave, minimizing phase errors and side lobes for a highly directive beam. With 3D-PDCH loading, the gain is enhanced by more than 200% in the desired band, with a realized peak gain of 22.3 dB at 27.5 GHz. The achieved 3-dB gain bandwidth (GBW) and fractional bandwidth (FBW) are 35.6% and 20.6%, respectively. The total efficiency is more than 80% with MIMO performance parameters (ECC <0.02 and ME $\approx ~0.5$ dB) in the desired band ensures its application in the mm-Wave band. To demonstrate the idea, the antenna is designed, fabricated, and measured at the mm-Wave band.

Abstracts:In direct current gas-insulated switchgear (dc-GIS), surface charge accumulation on insulating spacers is a significant challenge. Accumulated charges due to high dc electric fields distort the electric fields and decrease the flashover voltage. With surface charge measurements and numerical simulations, it is revealed that multiple factors, such as electric field strength and temperature, affect the charging profile. The objective of this article is to investigate the charge accumulation characteristics of insulating spacers with different shapes through experiments and calculations and to elucidate the charging mechanism. Surface charge distributions on downsized cone-type and disk-type model spacers under a simulated dc-GIS environment are measured and compared. Additionally, simulations considering the spacer bulk and insulating gas are conducted. Based on the measurements and simulations, the dominant charge supply sources and charge transport paths responsible for the charging phenomena are discussed. The results show that the surface charge distribution profiles of the two types of spacers differ in terms of charge quantity, charging area, and time variations. These results indicate that the charges injected from the electrode are predominantly transported through the spacer bulk in cone-type spacers and through the spacer surface in disk-type spacers. The dominant charge transport path and the intensive charge accumulation are greatly influenced by the initial electric field distribution, determined by the spacer geometry.

Abstracts:Air gaps are the principal insulating medium for transmission lines. The atmospheric conditions have an important nonlinear impact on the breakdown voltage prediction, such as temperature and humidity. Unlike previous studies that use a single model, this article introduces a weighted fusion approach, which has not previously been used to test the air gap insulation performance. This novel strategy assigns different importance levels to each model based on its strengths. The experimental breakdown voltage for plane–plane electrodes air gap is in good agreement with the estimated breakdown voltage using three neural network models: multilayer perceptron (MLP), radial basis function (RBF), and support vector regression (SVR). The performance of these models is measured using error metrics such as mean square error (mse), mean absolute percentage error (MAPE), mean square percentage error (MSPE), and root mse (RMSE), with the RBF model showing the best accuracy, reaching mse values of 1.4139% for humidity and 1.3855% for temperature. This fusion method significantly improves prediction accuracy, reducing mse to 0.5378% for humidity and 0.6268% for temperature. The results confirm that the proposed approach enhances prediction reliability and helps improve insulation performance in power systems.

Abstracts:Outdoor high-voltage (HV) insulators are prone to surface contamination, increasing flashover risk and threatening power transmission systems’ reliability. Timely inspection is essential, but conventional visual inspections are costly and inefficient for long transmission lines. An automated inspection using uncrewed aerial vehicle (UAV) images can be both efficient and low-cost. Given an appropriate training sample, a deep neural network, such as you only look once (YOLO), can be trained to localize insulators in diverse backgrounds and classify surface contamination. However, the wide variety of YOLO architectures proposed recently makes model selection challenging due to the accuracy and complexity tradeoffs. Rather than proposing a new model, this study focuses on selecting the “optimal” one. To this end: 1) a “laboratory” dataset of 15 000 insulator images with various surface contaminations is collected; 2) 21 YOLO architectures (YOLOv3–v11 and their variants) are trained, followed by a domain-specific (DS) model selection based on accuracy and complexity tradeoffs; and 3) the selected DS-YOLOv11m and DS-YOLOv11n are fine-tuned on a small “industry” dataset of 356 images from a local HV substation to validate DS pretraining in real-world scenarios. This work presents the first DS benchmarking of YOLOv3–v11 for insulator detection and contamination classification, resulting in a lightweight model optimized for real-time edge deployment. Evaluation of the fine-tuned models shows mAP@0.5 scores of 0.983 (DS-YOLOv11m) and 0.977 (DS-YOLOv11n). Although DS-YOLOv11n achieves slightly lower accuracy, it uses significantly fewer resources, reducing FLOPs by ~10.7 times. Finally, DS-YOLOv11n is deployed on a Raspberry Pi 5 to enable real-time contamination classification in edge computing scenarios.

Abstracts:The precise diagnosis of cable fault type and location before the fault occurs is crucial. To address the issues of pseudo-reflection interference and insufficient fault feature extraction accuracy in traditional cable fault detection methods, this article proposes a multispectrum joint localization method based on broadband impedance spectroscopy (BIS) and Morlet wavelet. By constructing a cable distributed parameter model, the impedance spectrum characteristics of four fault types-open-circuit, high-impedance, low-impedance, and short-circuit-are analyzed, including amplitude shift, phase mutation, and resonance pattern changes. A ridge smoothness optimization algorithm based on Morlet wavelet transform is proposed to effectively suppress interference from nonfault points, such as “pseudo-peaks” and ripple oscillations. Furthermore, a multispectrum joint analysis mechanism combining the real part, imaginary part, and amplitude is established, and the particle swarm optimization (PSO) algorithm is applied to dynamically optimize the weight combination of the spectra, overcoming the limitations of traditional single-spectrum analysis. Experimental results show that when a typical fault is set at 70 m of a 100 m coaxial cable, the relative localization error is within 0.7%, which is more than 60% more accurate than the traditional impedance method. Additionally, a fault type recognition criterion is proposed, which differentiates fault types based on impedance spectrum feature differences, and the effectiveness and accuracy of this method have been verified.

Abstracts:The presence of partial discharge (PD) activities deteriorates the high-voltage cross-linked polyethylene (XLPE) cable due to void, crack, and contamination of impurities. The persistence of these discharges replicates several carbonyl channels in the form of an electrical tree. The growth rate of treeing phenomena implies a sudden and complete breakdown of in-service cable. Therefore, detecting various stages of electrical tree growths is essential for the safety and ensuring the reliable operation of the XLPE cable over a more extended period. This study focuses on classifying the electrical tree growth stages of a 33 kV, three-core XLPE cable insulation using a vision transformer with a window sequence merging (WSM-ViT) mechanism. Initially, electrical tree growth images are collected from the experimental setup and expanded using image augmentation techniques to address the limited availability of images. Subsequently, the augmented images are used to train the WSM-ViT model, and its performance is compared with the vision transformer (ViT) model. The results indicate that the WSM-ViT approach exhibited enhanced scalability and achieved an impressive recognition accuracy of 99.28%, outperforming the other models. Furthermore, the proposed model demonstrated reliability and effectiveness by identifying electrical tree growth in untrained images.

Abstracts:This article describes the two space charge (SC) measurement campaigns on flexible factory joints (FFJs) for HVDC extruded submarine cables carried out in 2024 at Hellenic Cables, Greece, and Jiangsu Zhongtian, China, in the framework of the activities of the IEEE DEIS Study Group on this topic created by the IEEE DEIS Technical Committee “HVDC cable systems (cables, joints and terminations).” The measurements, done by some members of the study group (SG) by means of different PEA cells on FFJs having same geometry, but constructed by two different manufacturers and joint techniques, exhibited a good signal to noise ratio, with clear signal also from the inner semicon. Moreover, the measurement results were consistent with each other and capable of detecting directly or indirectly—especially at higher than room temperature—some effects related to the sloping interface between cable insulation and restored joint insulation, the main signature feature of FFJs. Hence, these measurements seem to indicate that a new IEEE Standard on SC measurements on FFJs is feasible.

Abstracts:Power transformers, as oil-immersed equipment, are primarily diagnosed using dissolved gas analysis (DGA), a widely recognized and effective fault detection method. Over time, numerous approaches have been developed to identify transformer faults through DGA, with many incorporating artificial intelligence to enhance diagnostic accuracy. In this context, this study enhances the support vector machine (SVM) and k-nearest neighbors (KNNs) algorithms by integrating them into the iterative processes of the artificial bee colony (ABC) and crow search algorithms (CSAs) for fault identification. Chaos theory is embedded into the optimization framework to improve convergence and avoid local optima. Nine input vectors, derived from a dataset of 501 samples covering six fault types, are used to evaluate the enhanced algorithms. An improved binary CSA is employed to optimize the input vectors, effectively identifying the key features for fault diagnosis. The proposed enhancement achieved an accuracy exceeding 94%, demonstrating the potential of refined methodologies for practical transformer diagnostics.

Abstracts:In this study, experiments are conducted to analyze energy equivalence between ac and bipolar dc inclined plane tracking and erosion tests. Tracking and erosion tests are performed on in-house prepared nano/micro co-filled composites and on a commercial sample. Tests are carried out using the inclined plane tracking and erosion tester interfaced with a high-speed data acquisition system (DAS), which samples and acquires voltage and current signals at 50 kHz. Python code calculates energy at individual sampling points and sums the same over the entire test duration to arrive at overall energy. Efficient handling of the sampled large data by Python is demonstrated with execution time as low as 20 min for each run. Tests were performed at different voltages, and energies were computed and compared. Criticality of +dc tests are highlighted, with the importance of achieving critical test voltage to sustain scintillations. The study uses Python for data handling and computation of large data generated during laboratory testing. A facile technique for energy calculation is described here.