Search Results (128)

In the field of high-altitude operations, the frequent occurrence of fall accidents is usually closely related to safety measures such as the incorrect use of safety locks and the wrong installation of safety belts. At present, the manual inspection method cannot achieve real-time monitoring of the safety status of the operators and is prone to serious consequences due to human negligence. This paper designs a new type of high-altitude operation safety device based on the STM32F103 microcontroller. This device integrates ultra-wideband (UWB) ranging technology, thin-film piezoresistive stress sensors, Beidou positioning, intelligent voice alarm, and intelligent safety lock. By fusing five modes, it realizes the functions of safety status detection and precise positioning. It can provide precise geographical coordinate positioning and vertical ground distance for the workers, ensuring the safety and standardization of the operation process. This safety device adopts multi-modal fusion high-altitude operation safety monitoring technology. The UWB module adopts a bidirectional ranging algorithm to achieve centimeter-level ranging accuracy. It can accurately determine dangerous heights of 2 m or more even in non-line-of-sight environments. The vertical ranging upper limit can reach 50 m, which can meet the maintenance height requirements of most transmission and distribution line towers. It uses a silicon carbide MEMS piezoresistive sensor innovatively, which is sensitive to stress detection and resistant to high temperatures and radiation. It builds a Beidou and Bluetooth cooperative positioning system, which can achieve centimeter-level positioning accuracy and an identification accuracy rate of over 99%. It can maintain meter-level positioning accuracy of geographical coordinates in complex environments. The development of this safety device can build a comprehensive and intelligent safety protection barrier for workers engaged in high-altitude operations. Full article

Incremental energy demand, environmental constraints, restrictions in the availability of energy resources, economic conditions, and political impact prompt the power sector toward deregulation. In addition to these impediments, electric power competition for power quality, reliability, availability, and cost forces utilities to maximize utilization of the existing infrastructure by flowing power on transmission lines near to their thermal limits. All these factors introduce problems related to power network stability, reliability, quality, congestion management, and security in restructured power systems. To overcome these problems, power-electronics-based FACTS devices are one of the beneficial solutions at present. In this review paper, the significant role of FACTS devices in restructured power networks and their technical benefits against various power system problems such as load frequency control, voltage stability, and congestion management will be presented. In addition, an extensive discussion about the comparison between different FACTS devices (series, shunt, and their combination) and comparison between various optimization techniques (classical, analytical, hybrid, and meta-heuristics) that support FACTS devices to achieve their respective benefits is presented in this paper. Generally, it is concluded that third-generation FACTS controllers are more popular to mitigate various power system problems (i.e., load frequency control, voltage stability, and congestion management). Moreover, a combination of multiple FACTS devices, with or without energy storage devices, is more beneficial compared to their individual usage. However, this is not commonly adopted in small power systems due to high installation or maintenance costs. Therefore, there is a trade-off between the selection and cost of FACTS devices to minimize the power system problems. Likewise, meta-heuristics and hybrid optimization techniques are commonly adopted to optimize FACTS devices due to their fast convergence, robustness, higher accuracy, and flexibility. Full article

Maintenance scheduling of hydropower units is essential for ensuring the operational security and stability of large-scale cascaded hydropower systems and for improving the efficiency of water energy utilization. This study takes the Cascaded Hydropower System of the Lower Jinsha River (CHSJS) as a representative case, identifying four key challenges facing maintenance planning: multi-dimensional influencing factor coupling, spatial and temporal conflicts with generation dispatch, coordination with transmission line maintenance, and compound uncertainties of inflow and load. To address these issues, four strategic recommendations are proposed: (1) identifying and quantifying the impacts of multi-factor influences on maintenance planning; (2) developing integrated models for the co-optimization of power generation dispatch and maintenance scheduling; (3) formulating coordinated maintenance strategies for hydropower units and associated transmission infrastructure; and (4) constructing joint models to manage the coupled uncertainties of inflow and load. The strategy proposed in this study was applied to the CHSJS, obtaining the weight of the impact factor. The coordinated unit maintenance arrangements of transmission line maintenance periods increased from 56% to 97%. This study highlights the critical need for synergistic optimization of generation dispatch and maintenance scheduling in large-scale cascaded hydropower systems and provides a methodological foundation for future research and practical applications. Full article

Ultraviolet (UV) communication, characterized by non-line-of-sight (NLOS) scattering, holds substantial potential for enabling communication networking in unmanned aerial vehicle (UAV) formations within strong electromagnetic interference environments. This paper proposes a networking protocol for large-scale mobile ultraviolet communication networks (LSM-UVCN). In large-scale networks, the proposed protocol establishes multiple non-interfering transmission paths based on a connection matrix simultaneously, ensuring reliable space division multiplexing (SDM) and optimizing the utilization of network channel resources. To address frequent network topology changes in mobile scenarios, the protocol employs periodic maintenance of the connection matrix, significantly reducing the adverse impacts of node mobility on network performance. Simulation results demonstrate that the proposed protocol achieves superior performance in large-scale mobile UV communication networks. By dynamically adjusting the connection matrix update frequency, it adapts to varying node mobility intensities, effectively minimizing control overhead and data loss rates while enhancing network throughput. This work underscores the protocol’s adaptability to dynamic network environments, providing a robust solution for high-reliability communication requirements in complex electromagnetic scenarios, particularly for UAV swarm applications. The integration of SDM and adaptive matrix maintenance highlights its scalability and efficiency, positioning it as a viable technology for next-generation wireless communication systems in challenging operational conditions. Full article

The paper focuses on the experimental investigation of the impact of filtration and tribological parameters on the reliability, service life, and functional characteristics of gear mechanisms used in robotics. The primary objective was to analyze the importance of lubricant cleanliness in robotic transmission modules and to assess the effectiveness of filtration as a preventive and protective measure. As part of the research, a dedicated test rig was designed and developed. Based on the measurements and analyses performed, a significant correlation was confirmed between lubricant contamination levels and degradation phenomena in transmission modules. The study also highlights a sharp increase in contamination during the initial hours of operation, emphasizing the need for early intervention and continuous monitoring. The findings have strong practical potential and are highly relevant for manufacturers of robotic systems, maintenance service providers, and operators of automated production lines. The results contribute to increased system reliability and extended service life, reduced maintenance and repair costs, and improved environmental aspects of robotic system maintenance. Full article

Composite insulators are deployed globally for outdoor insulation owing to their light weight, excellent pollution resistance, good mechanical strength, ease of installation, and low maintenance costs. The core rod in composite long-rod insulators plays a critical role in both mechanical load-bearing and internal insulation for overhead transmission lines, and its performance directly affects the overall operational condition of the insulator. However, it remains susceptible to failures induced by complex actions of mechanical, electrical, thermal, and environmental stresses. This paper systematically reviews the major failure modes of core rods, including mechanical failures (normal fracture, brittle fracture, and decay-like fracture) and electrical failures (flashunder and abnormal heating of the core rod). Through analysis of extensive field data and research findings, key failure mechanisms are identified. Preventive strategies encompassing material modification (such as superhydrophobic coatings, self-diagnostic materials, and self-healing epoxy resin), structural optimization (like the optimization of grading rings), and advanced inspection methods (such as IRT detection, Terahertz (THz) detection, X-ray computed tomography (XCT)) are proposed. Furthermore, the limitations of current technologies are discussed, emphasizing the need for in-depth studies on deterioration mechanisms, materials innovation, and defect detection technologies to enhance the long-term reliability of composite insulators in transmission networks. Full article

We developed a fully automated key production line for smart manufacturing technologies based on the Internet of Things (IoT) and automatic optical inspection (AOI) to enable efficient and consistent production. The production line consists of seven processing stations: raw materials uploading, groove milling, laser marking, key tooth cutting, deburring, defects inspection, and a discharge station. IoT technology enables real-time monitoring and data transmission through a visual panel that displays the operational status of each station and provides immediate alerts in case of abnormalities for quick intervention. The defects inspection station ensures comprehensive quality checks, automatically stops the production line for detected defects, and prevents defective products from proceeding to subsequent stages. Chronological data are used to support predictive maintenance, production parameter optimization, and energy efficiency improvements. Overall, the system effectively integrates automation, real-time monitoring, and quality control to ensure stable production and high product quality. Full article

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. Full article

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. Full article

The global transition toward clean energy has driven the extensive deployment of overhead tower-lines in desserts, where such structures face unique challenges from wind–sand interactions. The current design standards often overlook these combined loads due to oversimplified collision models and inadequate computational frameworks. These gaps are bridged in the present study through the development of a refined impact force model grounded in Hertz contact theory, which captures transient collision mechanics and energy dissipation during sand–structure interactions. Validated against field data from northwest China, the model enables a comprehensive parametric analysis of wind speed (5–60 m/s), sand density (1000–3500 kg/m³), elastic modulus (5–100 GPa), and Poisson’s ratio (0.1–0.4). Our results show that peak impact forces increase by 66.7% (with sand density) and 148% (with elastic modulus), with higher wind speeds amplifying forces nonlinearly, reaching 8 N at 30 m/s. An increased elastic modulus shifts energy dissipation toward elastic rebound, reducing the penetration depth by 28%. The dynamic analysis of a 123.6 m transmission tower under wind–sand coupling loads demonstrated significant structural response amplifications; displacements and axial forces increased by 28% and 41%, respectively, compared to pure wind conditions. These findings reveal the importance of integrating coupling load effects into design codes, particularly for towers in sandstorm-prone regions. The proposed framework provides a robust basis for enhancing structural resilience, offering practical insights for revising safety standards and optimizing maintenance strategies in arid environments. Full article

Efficient insulator-defect detection in transmission lines is crucial for ensuring the reliability and safety of power systems. This study introduces YOLOv8-IDX (You Only Look Once v8—Insulator Defect eXtensions), an enhanced DL (Deep Learning) based model designed specifically for detecting defects in transmission line insulators. The model builds upon the YOLOv8 framework, incorporating advanced modules, such as C3k2 in the backbone for enhanced feature extraction and C2fCIB in the neck for improved contextual understanding. These modifications aim to address the challenges of detecting small and complex defects under diverse environmental conditions. The results demonstrate that YOLOv8-IDX significantly outperforms the baseline YOLOv8 in terms of mean Average Precision (mAP) by 4.7% and 3.6% on the IDID and CPLID datasets, respectively, with F1 scores of 93.2 and 97.2 on the IDID and CPLID datasets, respectively. These findings underscore the model’s potential in automating power line inspections, reducing manual effort, and minimizing maintenance-related downtime. In conclusion, YOLOv8-IDX represents a step forward in leveraging DL and AI for smart grid applications, with implications for enhancing the reliability and efficiency of power transmission systems. Future work will focus on extending the model to multi-class defect detection and real-time deployment using UAV platforms. Full article

Water allocation under uncertainty remains a critical challenge in water-scarce regions. This study presents an integrated water allocation model that explicitly incorporates uncertainty through stochastic streamflow simulations and addresses multiple objectives—efficiency, equity, and sustainability—within a unified framework. The model uses historical inflow data, future demand projections, and a multi-objective optimization approach based on the NSGA-II to generate trade-off solutions. To support decision-making, TOPSIS is applied to identify the most balanced allocation strategies from the Pareto-optimal sets. The model is applied to the Namhan River Basin in South Korea, with two key applications: (1) developing adaptive water allocation strategies under dry, normal, and wet hydrological conditions, and (2) proposing targeted infrastructure enhancements—including new dams, transmission lines, and intake points—to address vulnerabilities in dry years. The results demonstrate that the proposed model improves supply reliability, economic efficiency, equity across regions, and sustainability through river maintenance and reservoir storage compliance. This study provides a generalizable and practical decision-support tool for long-term water planning under climate and demand uncertainties, offering actionable insights for water-deficient basins. Full article

As an indispensable component of power transmission systems, the performance of porcelain insulators directly impacts the reliability and operational stability of electrical networks. To enhance the anti-fouling flashover capability of transmission lines, porcelain insulators are commonly coated with RTV (Room Temperature Vulcanizing) silicone rubber coatings. However, the unique environmental conditions prevalent in high-altitude regions, such as those in Qinghai, impose distinctive stresses on the performance and longevity of RTV coatings. This study provides an in-depth analysis of the operational and aging characteristics of RTV-coated porcelain insulators under such extreme conditions, with a focus on evaluating performance degradation mechanisms. By conducting comprehensive field investigations of climatic and environmental parameters in Qinghai, this research elucidates the effects of high-altitude environments on RTV coatings and proposes a systematic methodology for detecting and analyzing aging-related phenomena. This study discerns the aging patterns of insulator surface coatings in high-altitude regions and examines the pollution accumulation mechanisms of insulators under adverse climatic conditions. These findings offer critical insights for optimizing the selection, maintenance, and replacement strategies of insulators in high-altitude transmission networks, thereby enhancing the safety, reliability, and operational efficiency of power systems in such challenging environments. Full article

The AC/DC transmission system is an important component of the power system, and the cross-circuitry Fault diagnosis of the AC/DC transmission system plays an important role in ensuring the normal operation of power equipment and personal safety. The traditional AC/DC transmission detection methods have the characteristics of complex detection processes and low fault line identification rates. Aiming at such problems, this paper proposes a new method of cross-circuitry Fault diagnosis based on the AC/DC transmission system based on a blind signal separation algorithm. Firstly, the method takes the typical cross-circuitry Fault scenario as an example to construct the topology diagram of the AC/DC power transmission system. Then, the electrical signals of the AC system and the DC system of the AC/DC power transmission system are collected, and the collected signals are extracted by the blind signal separation algorithm. Then, aiming at the cross-circuitry Fault problem of the DC system, the electrical quantities of the positive and negative poles on the rectifier side and the inverter side are collected, and the characteristics of the electrical quantities are analyzed by wavelet to determine the fault. At the same time, aiming at the problem of the cross-circuitry Fault of the AC system, three fault types of cross-circuitry Fault, ground fault, and intact fault are set up, and the electrical quantities of A, B, and C are collected on the same side, and the characteristics of three-phase electrical quantities are analyzed by wavelet. Finally, the cross-circuitry Fault judgment interval of the AC/DC system is set as the basis of fault judgment. After experimental verification, the relative error of the model is 1.4683%. The crossline fault identification method of the AC/DC transmission system based on the blind source separation algorithm proposed in this paper can accurately identify the crossline fault location and identify the fault type. It also provides theoretical and experimental support for power system maintenance personnel to maintain equipment. Full article

Power transmission lines transfer energy between power plants and substations by means of a linear chain of towers. These towers are often situated over extensive distances, sometimes in regions that are difficult to access. Wireless sensor networks present a viable solution for monitoring these long chains of towers due to their wide coverage, ease of installation and cost-effectiveness. The proposed LoRaBUS approach implements and analyses the benefits of a linear topology using a mixture of LoRa and LoRaWAN protocols. This approach is designed to enable automatic detection of nearby nodes, optimise energy consumption and provide a prioritised transmission mode in emergency situations. On remote, hard-to-reach towers, a prototype fire protection system was implemented and tested. The results demonstrate that LoRaBUS creates a self-configurable linear topology which proves advantageous for installation processes, node maintenance and troubleshooting node failures. The discovery process collects data from a neighbourhood to construct the network and to save energy. The network’s autonomous configuration can be completed within approximately 2 min. In addition, energy consumption is effectively reduced 25% by dynamically adjusting the transmission power based on the detected channel quality and the distance to the nearest neighbour nodes. Full article