Transmission Line Research Articles

Blend Electrodes in Lithium‐Ion Batteries: Investigation of Inhomogeneities Using a Spatially Resolved Transmission Line Model

Blend electrodes are used in lithium‐ion batteries to increase the performance by combining two active materials, such as silicon (Si) or silicon oxide (SiOx) and graphite (Gr) for the negative electrode. In‐depth knowledge of the complex interactions between the materials is essential to understand how inhomogeneities and local peaks of the intercalation current arise and how they can be prevented. This work presents a spatially resolved transmission line model developed to describe the electrochemical behavior of blend electrodes. Parameterization and model validation are carried out for a Gr/SiOx anode. Simulation results are used to investigate inhomogeneities in local states during lithiation at different C‐rates. A special focus is put on stress indicators as precursors for accelerated aging like local materialspecific C‐rates and spatial gradients of the degree of lithiation. Thus, the modeling approach is a tool for both the description of the properties of blend electrodes and for simulation‐based balancing of the active materials’ capacities within blend electrodes.

A Data-Driven Approach to Voltage Stability Support via FVSI-Based Distributed Generator Placement in Contingency Scenarios

This research presents a novel methodology based on data analysis for improving voltage stability in transmission systems. The proposal aims to determine a single distributed generator’s optimal location and sizing using the Fast Voltage Stability Index (FVSI) as the primary metric under N−1 contingency conditions. The developed strategy systematically identifies the most critical transmission lines close to instability through a frequency analysis of the FVSI in the base case and across multiple contingency scenarios. Subsequently, the weak buses associated with the most critical line are determined, on which critical load increases are simulated. The Distributed Generator (DG) sizing and location parameters are then optimized through a statistical analysis of the inflection point and the rate of change of the FVSI statistical parameters. The methodology is validated in three case studies: IEEE systems with 14, 30, and 118 buses, demonstrating its scalability and effectiveness. The results show significant reductions in FVSI values and notable improvements in voltage profiles under stress and contingency conditions. For example, in the 30-bus IEEE system, the average FVSI for all contingency scenarios was reduced by 26% after applying the optimal solution. At the same time, the voltage profiles even exceeded those of the base case. This strategy represents a significant contribution, as it is capable of improving the stability of the electrical power system in all N−1 contingency scenarios with overload at critical nodes. Using a single DG as a low-cost and highly effective corrective measure, the proposed approach outperforms conventional solutions through statistical analysis and a data-centric approach.

Overview of the Research on Improved YOLO Model in Insulator Defect Detection

People’s everyday lives depend on electricity, and insulators are essential to the reliable and safe functioning of high-voltage transmission lines. Therefore, defect detection of insulators has always been a concern for many research teams and researchers. The You Only Look Once(YOLO) series algorithm has always been the most widely used algorithm in the research of insulator defect detection. This paper seeks to examine the future directions for enhancing the YOLO model, evaluate the research problems in the field of insulator defect detection, and methodically study the pertinent theories for doing so. First, the paper outlines the main research progress in the field of insulator defect detection. Then focus on discussing the basic structure, dataset, improvement research, and experimental results of the three generations of YOLOv3, YOLOv5, and YOLOv8 models. Based on the above review, the paper proposes directions for enhancing the dataset of insulator defects and provides insights on improving the model for insulator defect detection. In addition, this article also points out the difficulties in collecting datasets in current research, providing research suggestions for future researchers.

RFID Sensor with Integrated Energy Harvesting for Wireless Measurement of dc Magnetic Fields.

High-voltage direct-current (HVdc) transmission lines are gaining more attention as an integral part of modern power system networks. Monitoring the dc current is important for metering and the development of dynamic line rating control schemes. However, this has been a challenging task, and there is a need for wireless sensing methods with high accuracy and a dynamic range. Conventional methods require direct contact with the high-voltage conductors and utilize bulky and complex equipment. In this paper, an ultra-high-frequency (UHF) radio frequency identification (RFID)-based sensor is introduced for the monitoring of the dc current of an HVdc transmission line. The sensor is composed of a passive RFID tag with a custom-designed antenna, integrated with a Hall effect magnetic field device and an RF power harvesting unit. The dc current is measured by monitoring the dc magnetic field around the conductor using the Hall effect device. The internal memory of the RFID tag is encoded with the magnetic field data. The entire RFID sensor can be wirelessly powered and interrogated using a conventional RFID reader. The advantage of this approach is that the sensor does not require batteries and does not need additional maintenance during its lifetime. This is an important feature in a high-voltage environment where any maintenance requires either an outage or special equipment. In this paper, the detailed design of the RFID sensor is presented, including the antenna design and measurements for both the RFID tag and the RF harvesting section, the microcontroller interfacing design and testing, the magnetic field sensor calibration, and the RF power harvesting section. The UHF RFID-based magnetic field sensor was fabricated and tested using a laboratory experimental setup. In the experiment, a 40 mm-diameter-aluminum conductor, typically used in 500 kV HVdc transmission lines carrying a dc current of up to 1200 A, was used to conduct dc current tests for the fabricated sensor. The sensor was placed near the conductor such that the Hall effect device was close to the surface of the conductor, and readings were acquired by the RFID reader. The sensitivity of the entire RFID sensor was 30 mV/mT, with linear behavior over a magnetic flux density range from 0 mT to 4.5 mT.

Improved Prediction Model for the Corrosion of Aluminium Alloy Conductors: Considering the Influence of Electric Field and Dynamic Boundary

ABSTRACTThe corrosion problems of high‐voltage power transmission conductors typically occur in environments with electric fields. However, current research mainly focuses on atmospheric corrosion of metals with limited attention to the combined effects of electric fields and atmospheric conditions on metal corrosion. This study established a corrosion prediction model that considers the effects of electric fields and dynamic boundaries. Because of the influence of dynamic boundaries, this model can calculate parameters such as corrosion rate, corrosion depth, corrosion product accumulation and ion concentration for metal samples with and without an external electric field. The model is validated through indoor accelerated corrosion tests under low applied electric fields and by using aluminium alloy conductor samples from high electric field regions of actual ± 500 kV power transmission lines. The results indicate that the corrosion rate of aluminium alloys initially increases and then decreases over time. Additionally, the corrosion rate of aluminium alloys under an applied electric field is higher than that without an electric field during the same period. The mechanism of increased corrosion rate is analysed to be that the presence of the electric field accelerates the cathode reaction rate of the electrode. The corrosion rate of the sample increased by about 78% under a lower electric field (0–20 kV/m) and by about 2.75 times under a higher electric field around 2000 kV/m.

Impact of Electromagnetic Pulses on N-Type MOSFET Reliability: Experimental Insights

In power systems, MOSFET devices used in industrial chips exhibit more pronounced degradation when subjected to intense electromagnetic pulses than in conventional environments. Conventional reliability testing methods, which fail to simulate dynamic electromagnetic environments, are unable to accurately assess the changes in device performance under electromagnetic interference. In this study, we employed a transmission line pulse generator to apply pulse stress to N-type MOSFET devices, systematically investigating the degradation mechanisms by varying pulse features such as pulse cycle, amplitude, rise/fall times, and intervals. The results indicate that changes in the electrical properties of the devices are primarily influenced by two types of charged traps. Under the conditions of low pulse cycles, the current response of the devices may even exceed that prior to stress application. The study further analyzed the competitive mechanisms of these different traps during the device degradation process. Additionally, by varying the test temperature to mimic industrial application scenarios, we analyzed the degradation behavior of the devices under multi-physics conditions.

Noninvasive blood glucose monitoring using a dual band microwave sensor with machine learning

The potential for continuous non-invasive blood glucose monitoring has attracted a lot of interest in the field of medical diagnostics. This paper provides a new shape of a dual-band bandpass filter (DBBPF) acting as a microwave transmission line sensor for continuous non-invasive blood glucose monitoring operating at 2.45 and 5.2 GHz. The proposed system uses the interaction between biological tissues and microwave signals to correctly assess blood glucose levels. The proposed dual-band bandpass filter (DBBPF), comprises three split ring resonator (SRR) cells with different dimensions. It is designed to operate as a sensor with improved sensitivity, compact dimensions, and a high-quality factor. It also ensures a reasonable bandwidth for lower and higher bands of 8.6 and 2%, respectively in the industrial, scientific, medical band, and the wireless local area network (ISM and WLAN) Bands. A dual-band filter enhances measurement sensitivity and specificity by targeting specific frequency ranges where glucose exhibits distinctive dielectric responses, thereby providing redundant data points for accurate glucose level determination. Glucose concentrations can be evaluated by measuring the changes in the dielectric properties of blood by sending microwave waves through the body and assessing the collected S-parameter signals. The measurement parameters encompass the reflection, phase, magnitude, as well as transmission parameters. This yields multiple evaluations of the glucose-induced alterations. Simulations are validated through laboratory measurements incorporating a phantom finger model for capturing realistic outcomes. Machine learning models are employed to analyze the sensor data, improving the accuracy of diabetes detection. Simulations are validated through laboratory measurements incorporating a phantom finger model for capturing realistic outcomes. A Cole-Cole model, implemented using MATLAB, is utilized for the phantom finger model. The main results reveal the success of the proposed transmission-based microwave glucose sensing, with a remarkable sensitivity of 1 ~ 1.5 dB for glucose level change up to 200 mg/dL.

Design of Digital Simulation Monitoring System for High-Voltage Power Transmission Lines with Ad-hoc MAC Access

To realize the digital simulation monitoring for high-voltage transmission lines, a transmission line monitoring system based on the Media Access Control (MAC) mode of a wireless Ad-hoc network is studied. We analyze the transmission services of transmission lines comprehensively and adopt the Linear Hybrid Time Division Multiple Access (LHTDMA) hybrid access mode with logic integration and services to improve the resource utilization efficiency of the algorithm. In the optimization process of the routing algorithm, the optimization scheme based on deep Q-learning is selected to enhance the system life cycle and reduce the system delay. The algorithm of this study is verified by experiments. The experimental results confirm that the LHTDMA hybrid access method has higher data throughput and lower delay effect. Compared with other routing algorithms, under the same conditions, the time cost of this algorithm is lower than other comparison algorithms. As the size of the transfer file gradually increases, the advantages of this algorithm are more obvious for its more accurate iterative output results, which proves its superiority.

Understanding negative-bias-stress-induced instability and hump phenomenon in amorphous In–Ga–Zn–O thin-film transistors: Impact of source/drain contacts and carrier diffusion

Reliability and stability of oxide thin-film transistors (TFT) are a longstanding unsettled research topic and are critical for practical device applications. This work discussed negative gate bias stress (NBS)-induced instability, including the bias-induced hump phenomenon for amorphous In–Ga–Zn–O (a-IGZO)-TFT. To identify the key factors contributing to NBS instability, we quantitatively evaluated lateral electron diffusion behavior in the a-IGZO channel by transmission line method (TLM) analysis and developed a critical channel length scale model. The effective source/drain (S/D) contact diffusion length (2LD) was extracted, where the lateral carrier distribution (ne) profiles were depicted, showing that nLD was ∼6.5 × 1017 cm−3. The LD was 3%–5% proportional to the maximum extra-carrier spreading length scale (LC) from S/D extension region into the channel, affected >20% of each side of the lateral channel area, and made significant impacts on the TFT stability. This model successfully explained the notable threshold (Vth) roll-off characteristics and mobility variations in the shorter-channel devices. Short-term and long-term NBS stability tests found that short-channel devices with a 5 μm length exhibited large negative Vth shifts accompanied by hump behavior and broadened hysteresis window due to LD and LC diffusion influence. Based on the proposed carrier diffusion model, the NBS-induced TFT instability was attributed to the formation of a highly conductive back-channel layer originating from the electron accumulation by lateral carrier diffusion from S/D region extension. This study provided a quantitative insight into NBS-induced microscopic changes in the a-IGZO channel, contributing to the understanding of bias-instability and the development of a highly stable oxide-TFTs.

Transmission Line Monitoring Using Computer Vision & AI

Unmanned aerial vehicles (UAVs)with artificial intelligence (AI) can provide a revolutionary solution for the monitoring and inspection of power transmission lines. This study employs the YOLOv5 deep learning model to detect faults from custom datasets for the context of Nepal, where rugged terrains impede the feasibility of inspections. Some major faults we are proposing to inspect are broken insulators, vegetation encroachment, conductor sag, and corona losses. We used 950 bounding boxes in 400 images annotated manually, and augmented data was used for model robustness. The evaluation demonstrated the system was precise and accurate, demonstrating the system has the potential to reliably detect a fault. First, this research advances the state of the art in AI-driven infrastructure monitoring by proposing a scalable, efficient, and context-aware system for enhancing Nepal’s energy transmission reliability.

Hybrid threats in the Baltic Sea. The results of analysis of countermeasure options

The maritime areas of the Baltic region, can be considered a space for the conduct of proxy conflict, i.e. of a substitute nature, between the Russian Federation and Western states. Intensifying since 2022 (the start of Russia’s full-scale invasion of Ukraine) hybrid actions on the part of Russia have largely targeted the critical infrastructure facilities of coastal states. The article discusses the various forms of subliminal actions against this infrastructure undertaken by Russia, based on available information on events in the period from 2022 to the end of 2024. It also presents the vulnerabilities to hybrid actions that characterise critical infrastructure in the Baltic (possible ways of affecting port facilities and offshore infrastructure, with a particular focus on undersea transmission lines). The publication also presents the author’s views on possible responses from NATO countries, in the form of political, military and technological initiatives, to hybrid threats posed by modern maritime autonomous systems.

Biomimetic Xanthium Strumarium Inspired Superhydrophobic Anti-/De-Icing Films with Near-Infrared Light-Induced Self-Healing.

Superhydrophobic surfaces are susceptible to structural deformation and damage during use, which significantly impacts their long-term stability and performance in anti-/de-icing applications. To address this challenge, a biomimetic superhydrophobic polyurethane film inspired by Xanthium strumarium (PBXS) is proposed. This film not only delivers efficient anti-/de-icing performance but also demonstrates exceptional long-term durability. Even when the surface structure undergoes deformation or complete fracture, it can quickly self-heal under near-infrared light, restoring its original properties. The results show that, due to the superhydrophobic micron spine array and photothermal effect, PBXS can delay droplet freezing at low temperatures (-10°C, 2052 s) and enable rapid de-icing (1 sun, 187 s). Moreover, by incorporating the shape-memory properties of thermoplastic polyurethane and self-healing capability, PBXS effectively addresses issues related to surface deformation (after ten deformation-healing cycles, PBXS maintains a water contact angle of 157 ± 1° and a rolling angle of 15.4 ± 1°) and material rupture (after ten fracture-healing cycles, PBXS retains a water contact angle of 152 ± 1° and a rolling angle of 16.1 ± 1°). This innovative approach enhances the long-term performance of anti-/de-icing films and shows significant potential for applications in road transportation, power transmission lines, and other anti-/de-icing fields.

The impact of high voltage transmission lines (HVTL) on property value: A descriptive initial survey

Properties hosting High Voltage Transmission Lines (HVTL) generally have lower values in Turkey. Therefore, owners are compensated for the easement. However, due to the inadequacy of laws and regulations, easement prices (compensation) can be calculated incorrectly. In this study, a descriptive survey was conducted to obtain ideas and information on the solution of problems encountered in determining easement prices and practices in other countries were examined. As a result of the survey, the area covered by the easement on the property and the route of the line were identified as the most important factors that negatively affect property value. It emerges that the diagonal crossing of HVTL over properties is the factor that decreases property value the most (compared to other crossing methods). Additionally, it is observed that the easement price paid to property owners varies mostly according to the per square meter value of the property and secondly according to the area of the property.

Performance Evaluation and Calibration of Electromagnetic Field (EMF) Area Monitors Using a Multi-Wire Transverse Electromagnetic (MWTEM) Transmission Line.

The exposure levels generated by environmental electromagnetic field (EMF) sources can be measured and monitored by employing EMF area monitors. The operating spectrum of environmental EMF sources is not limited to high frequencies (f > 30 MHz) but also extends to low frequencies (f < 30 MHz), where sources associated, for example, with radio transmitters typically generate non-negligible field contributions. For this reason, professional EMF area monitors can be equipped with different field sensors, properly calibrated according to standardized procedures. Because low-frequency electric fields are very sensitive to environmental boundary conditions, equipping an EMF area monitor with electric field sensors, previously calibrated as stand-alone devices, can lead to measurement errors due to field perturbations introduced by the physical structure of the area monitor itself. This paper describes the activities carried out to assess the performance of an EMF area monitor in simulated realistic conditions and calibrate it in the 300 kHz-20 MHz frequency band. The activities were conducted using a multi-wire transverse electromagnetic (MWTEM) transmission line as a controlled electric field source, with dimensions suitable for exposure of the entire structure of the EMF area monitor. In view of using this approach to calibrate the area monitors as a whole instead of the individual sensors, the uniformity of the electric field generated by the available MWTEM transmission line was analyzed in detail both numerically and experimentally. Finally, the results of the evaluation and calibration of an area monitor are reported and discussed.

Deep Learning Image Compression Method Based On Efficient Channel-Time Attention Module

Remote monitoring of transmission lines plays a vital role in ensuring the stable operation of power systems, especially in regions with weak or unstable network signals, where efficient data transmission and storage are essential. However, traditional image compression methods face significant limitations in both quality and efficiency when applied to high-resolution imagery in such scenarios.To address these challenges, this paper proposes a deep learning–based image compression approach incorporating an Efficient Channel-Temporal Attention Module (ETAM). The ETAM module integrates Efficient Channel Attention (ECA-Net) and a Temporal Attention Module (TAM) to jointly enhance the extraction of spatial and temporal features, thereby improving compression efficiency and reconstruction quality.Experimental results demonstrate that the proposed method consistently outperforms both traditional and state-of-the-art deep learning–based compression techniques across multiple evaluation metrics, including PSNR, SSIM, and LPIPS. Notably, evaluations on the STN PLAD dataset show that ETAM better preserves fine-grained details and textures, even under high compression ratios, resulting in reconstructions that closely resemble the original images.These findings underscore the practical potential of the ETAM method for efficient, high-quality image compression in real-world applications such as transmission line monitoring under constrained network conditions.

Frequency-detuned exceptional surface assisted by the self-coherence effect of a giant atom.

Exceptional points (EPs), singularities in non-Hermitian systems where eigenvalues and eigenvectors coalesce, have enabled groundbreaking phenomena such as unidirectional invisibility, chiral transmission, and enhanced sensing. However, their sensitivity to perturbations, particularly frequency detuning, poses a significant challenge in realizing robust EPs. While exceptional surfaces (ESs) have been proposed to mitigate this issue, frequency consistency remains a critical requirement for achieving EPs in real-coupling systems. Here, we address this limitation by introducing a giant atom-based system, leveraging the unique self-coherent coupling effects of giant atoms to construct frequency-detuned EPs and ESs in real-coupling systems. Using a split-ring resonator coupled to a meandering microwave transmission line, we experimentally demonstrate an ES in a frequency-detuned regime. The giant atom's multi-point non-local coupling enables precise control over frequency detuning and linewidth, offering a new degree of freedom for engineering non-Hermitian degeneracies. Our results not only expand the scope of ESs but also highlight the potential of giant atoms in exploring novel non-Hermitian photonics, including synthetic dimensions, chirality reversal, and stable EP-enhanced sensors.

A Two-Stage YOLOv5s-U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines.

Transmission-line defect detection is crucial for grid operation. Existing methods struggle to balance defect localization and fine segmentation. Therefore, this study proposes a novel cascaded two-stage framework that first utilizes YOLOv5s for the global localization of defective regions, and then uses U-Net for the fine segmentation of candidate regions. To improve the segmentation performance, U-Net adopts a transfer learning strategy based on the VGG16 pretrained model to alleviate the impact of limited dataset size on the training effect. Meanwhile, a hybrid loss function that combines Dice Loss and Focal Loss is designed to solve the small-target and class imbalance problems. This method integrates target detection and fine segmentation, enhancing detection precision and improving the extraction of detailed damage features. Experiments on the self-constructed dataset show that the method achieves 87% mAP on YOLOv5s, 88% U-Net damage recognition precision, a mean Dice coefficient of 93.66%, and 89% mIoU, demonstrating its effectiveness in accurately detecting transmission-line defects and efficiently segmenting the damage region, providing assistance for the intelligent operation and maintenance of transmission lines.

Avaliação da Estabilidade de Tensão Utilizando o Índice |D’| e Redes Neurais Artificiais sob Contingências

Approaches using Artificial Neural Networks (ANNs) have aimed to enhance the accuracy and reliability of voltage stability index calculations to ensure the secure operation of Power Systems (PS), especially under conditions of imminent voltage collapse. Furthermore, complementary research has incorporated the dynamic modeling of transformers and renewable energy sources, while also leveraging real-time phasor measurements to enhance these methodologies. Despite significant advancements, there is still a need to improve the accuracy and computational efficiency of existing indices, particularly in multiple contingency scenarios. This paper proposes the use of the |D’| index, derived from the Power Flow Jacobian matrix, to enhance the precision of voltage stability assessment. The proposed method is evaluated through simulations considering three types of contingencies: stepwise increase in active and reactive power at loads, continuation power flow analysis, and transmission line outages. Performance tests of the |D’| index, using ANNs, demonstrate high accuracy and strong generalization, with low mean absolute errors and standard deviation values, enabling the efficient identification of the most critical buses in the system with low computational cost. The proposed method proved to be effective in reducing errors and variance during testing and validation, particularly under operating conditions close to voltage collapse, highlighting its robustness and efficiency in real-time stability analysis.

A new random convolutional Kernel-based system for transmission line fault classification

A new random convolutional Kernel-based system for transmission line fault classification

High voltage direct current system-based generation and transmission expansion planning considering reactive power management of AC and DC stations

This study presents a planning approach that considers the simultaneous expansion of generating and transmission systems, taking into account the location and sizing of generation units, AC transmission lines, and high-voltage direct-current (HVDC) systems. The HVDC system utilizes AC and DC substations equipped with AC/DC and DC/AC power electronic converters, respectively, to effectively regulate and control the reactive power of the transmission network. The problem aims to minimize the combined annual cost of constructing the specified parts and operating the generation units. This is subject to constraints such as the size and investment budget limits, an AC optimum power flow model, and the operational limits of both renewable and non-renewable generation units. The scheme incorporates a non-linear model. The Red Panda Optimization (RPO) is utilized to solve the provided model in order to attain a dependable and optimal solution. This research focuses on several advances, including the planning of the HVDC power system, the regulation of reactive power in HVDC substations, and the resolution of related issues using the RPO algorithm. The numerical findings collected from several case studies demonstrate the effectiveness of the suggested approach in enhancing the economic and technical aspects of the transmission network. Efficiently coordinating the generation units, AC transmission lines, and HVDC system leads to a significant enhancement in the economic performance of the network, resulting in a 10–40% improvement compared to the network power flow studies.