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During deicing operations on transmission lines, the cutting forces generated by the milling cutter of a deicing robot exert significant reaction forces on the robot body. Excessive cutting forces can compromise the robot's locomotion stability and deicing performance. This study introduces an optimization of the traditional straight-plate milling cutter by designing two new types of deicing milling cutters: oblique-cut and straight-cut milling cutters. The effects of cutter geometry, milling speed, and feed rate on cutting forces were systematically investigated using finite element simulations. A deicing test platform was constructed to validate the simulation results. The findings indicate that the cutting force hierarchy among the three designs is as follows: straight-plate > oblique-cut > straight-cut. Notably, the straight-cut milling cutter reduces cutting forces by 16–33% compared with the traditional straight-plate cutter. Furthermore, higher milling speeds and faster feed rates along the transmission line increase cutting forces. These studies provide valuable guidance for optimizing milling cutter designs in deicing robots. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Architecture & Civil Engineering is the property of Chang'an Daxue Zazhishe and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To maintain the reliability of power systems, routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues. The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods, especially in identifying small objects in high-resolution images. This study presents an enhanced object detection algorithm based on the Faster Region-based Convolutional Neural Network (Faster R-CNN) framework, specifically tailored for detecting small-scale electrical components like insulators, shock hammers, and screws in transmission line. The algorithm features an improved backbone network for Faster R-CNN, which significantly boosts the feature extraction network's ability to detect fine details. The Region Proposal Network is optimized using a method of guided feature refinement (GFR), which achieves a balance between accuracy and speed. The incorporation of Generalized Intersection over Union (GIOU) and Region of Interest (ROI) Align further refines the model's accuracy. Experimental results demonstrate a notable improvement in mean Average Precision, reaching 89.3%, an 11.1% increase compared to the standard Faster R-CNN. This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images. [ABSTRACT FROM AUTHOR]

This paper examines the modeling approaches used to analyze the electric field distribution in high-voltage direct-current gas-insulated systems (HVDC-GISs) used for the acceleration grid power supply (AGPS) of neutral beam injectors (NBIs). A key challenge in this context is the degradation of dielectric performance due to radiation-induced conductivity (RIC), a phenomenon specific to the harsh radioactive environments near fusion reactors. Traditional models for gas conductivity in HVDC-GISs often rely on constant or nonlinear conductivity formulations, which are based on experimental data but fail to capture the effects of external ionizing radiation that triggers RIC. To address this limitation, a more advanced approach, the drift–diffusion recombination (DDR) model, is used, as it more accurately represents gas ionization and the influence of radiation fields. However, this increased accuracy comes at the cost of higher computational complexity. This paper compares the different modeling strategies, discussing their strengths and weaknesses, with a focus on the capabilities in evaluating the charge accumulation and the RIC phenomenon. [ABSTRACT FROM AUTHOR]

Transmission line conventional current differential schemes are significantly impacted by the dynamic fault current features, transient behavior, and unpredictable power generation of large-scale doubly fed induction generator-based wind farms (DFIG-WFs). Further, combined effects of WFs and flexible alternating current transmission system (FACTS) controller have adverse effect on various unit protection schemes. Normal transmission lines have a current differential between the two ends that rises with line length. If the WFs power output is low, the capacitive charging current may match the load current. Conventional differential protection methods are facing difficulties in the presence of WFs. This research proposes a data-driven approach to determine the effects of several scenarios with significant offshore WFs coupled to transmission lines on differential relay characteristics. The per-phase differential current is calculated using the short-term matrix pencil method (STMPM) based signal reconstruction technique. The net change in the current magnitude is then determined by estimating the first- and second-order derivatives of this data. Lastly, a disturbance detection index (DDI) is derived from the Euclidean distance between these first and second derivative components. The PSCAD/EMTDC simulation studies that were carried out show that the suggested strategy functions satisfactorily for a variety of disturbances, such as internal and external faults, fault resistance, fluctuating fault position, WF penetration changes, and other types of non-fault switching events. [ABSTRACT FROM AUTHOR]

Ice shedding and vibration of ice-covered transmission lines can pose a serious threat to the safe operation of power lines in heavy ice areas. However, the common preventative practice of installing anti-vibration hammers, spacers and other devices on the line has limited effects on the safe operation of transmission lines. This paper focused on the simulation and experimental study of the influence of orthogonal double pendulum damper, a new type of anti-vibration device for transmission lines, on the ice shedding and vibration reduction of transmission lines. The 3-DOF calculation model of the orthogonal double pendulum damper was established, and the influence of the parameters of orthogonal double pendulum damper on the ice shedding of the line was simulated and analysed. In addition, natural ice shedding tests of lines were carried out at Xuefeng Mountain National Field Station, and ice shedding and vibration displacement response of lines was measured after the installation of orthogonal double pendulum dampers. The results showed that The three main structural parameters of mass, pendulum length and arm length can affect the maximum amplitude of ice shedding and vibration of transmission lines. The mass of the hammerhead has an effect on the vibration in both vertical and horizontal directions; the pendulum length mainly affects the vibration of the line in the horizontal direction; the arm length mainly affects the vibration in the vertical direction. [ABSTRACT FROM AUTHOR]

The failure of overhead transmission lines in the United States can lead to significant economic losses and widespread blackouts, affecting the lives of millions. This study focuses on analyzing the failure of transmission lines, specifically considering the effects of wind, ambient temperature, and current demands, incorporating minimal and significant pre-existing damage. We propose a multiphysics framework to analyze the transmission line failures across sensitive and affected states of the United States, integrating historical data on wind and ambient temperature. By combining numerical simulation with historical data analysis, our research assesses the impact of varying environmental conditions on the reliability of transmission lines. Our methodology begins with a deterministic approach to model temperature and damage evolution, using phase-field modeling for fatigue and damage coupled with electrical and thermal models. Later, we adopt the probability collocation method to investigate the stochastic behavior of the system, enhancing our understanding of uncertainties in model parameters, conducting sensitivity analysis to identify the most significant model parameters, and estimating the probability of failures over time. This approach allows for a comprehensive analysis of factors affecting transmission line reliability, contributing valuable insights into improving power line's resilience against environmental conditions. [ABSTRACT FROM AUTHOR]

Guyed towers in high-voltage transmission lines consist of the tower body, guy wire system, and foundation. A well-designed guy wire system with optimized tension levels is essential to maintain the stability of the tower under wind loads and other external forces. In practical operation, to prevent excessive corrosion of the pinned metal components at the tower base, these connections are often encased in concrete, altering the base connection conditions and affecting the structural forces on the tower. This study develops a finite element analysis model based on two guyed tower structures from a high-voltage transmission line project. By measuring the actual tensions of the guy wire and testing the basic material performance, this model considers the effects of varying base connection conditions and different guy wire tension levels. Under designed ice load and extreme wind load conditions, the analysis focuses on changes in tower body stress, tower-top displacement and inclination, and guy wire forces. The results indicate that when the tower base is uniformly pinned or fixed, the initial guy wire tension has minimal impact on maximum tower stress but significantly affects maximum tower displacement and inclination when the tower was under the ice and wind load conditions. The base connection condition has a pronounced impact on the stress states of the tower and guy wire system, especially under the designed wind loads. In particular, when the base is fixed, the maximum base stress in Tower 1 under the wind loads is 270% higher than in a pinned condition. The initial guy wire tension level significantly affects the guy wire force under the ice and wind loads; for example, when Tower 1 is subjected to approximately 85% of the design level of high wind load, some guy wires reach full relaxation prematurely, presenting localized strength failure risks at the tower foot, potentially threatening the tower safety under extreme design loads. [ABSTRACT FROM AUTHOR]

Aerial photography using unmanned aerial vehicles (UAVs) to detect foreign objects is an important method to ensure the safety of transmission lines. However, existing detection algorithms often encounter challenges in complex environments, including limited recognition capability and high computational demands. To address these issues, this paper proposes YOLO-LAF, a lightweight foreign object detection algorithm that is based on YOLOv8n and incorporates an innovative adaptive weight pooling technique. The proposed method introduces a novel adaptive weight pooling module within the backbone network to enhance feature extraction for detecting foreign objects on transmission lines. Additionally, a multi-scale detection strategy is designed to integrate the FasterBlock and EMA modules. This combination enables the model to effectively capture both global and local image features through cross-channel interactions, thereby reducing misdetection and omission rates. Furthermore, a C2f-SCConv module is introduced in the neck network to streamline the model by eliminating redundant features, thus improving computational efficiency. Experimental results demonstrate that YOLO-LAF achieves average accuracies of 91.2% and 85.3% on the Southern Power Grid and RailFOD23 datasets, respectively, outperforming the original YOLOv8n algorithm by 2.6% and 1.8%. Moreover, YOLO-LAF reduces the number of parameters by 23.5% and 14.8% and the computational costs by 19.9% and 24.8%, respectively. These improvements demonstrate the superior detection performance of YOLO-LAF compared to other mainstream detection algorithms. [ABSTRACT FROM AUTHOR]

The main objective of the work is to energize the wireless sensor network by using Radio Frequency energy harvesting. It has the potential to harvest the RF energy available in the surroundings. However, the available RF bands in the atmosphere are unpredictable. Therefore, a multiband harvester's design adds more power from the vast number of sub-operating bands. In this work, a multiband rectifier is designed to harvest energy from 1.28 GHz, 1.8 GHz, 2.44 GHz, 3.5 GHz, and 5 GHz frequency bands simultaneously. An enhanced impedance matching network is designed and validated. The purpose of this is to improve the performance of the rectifier. Multiband rectifier achieves higher conversion efficiency than the traditional single-band approach. The highest power conversion efficiency is measured at 89.08% at 0 dBm input power at 1.8 GHz operating band and 4.8 V DC power is received. This paper introduces a novel design structure as well as a simple circuit structure of a multiband rectifier. The proposed rectifier provides sufficient energy to power the sensors used in wireless sensor networks and many other low-power electronic devices in a series of potential applications. It also demonstrates the higher conversion efficiency with existing designs. [ABSTRACT FROM AUTHOR]

Faults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced. [ABSTRACT FROM AUTHOR]

Capacitively coupled plasmas (CCPs) are widely used in plasma processing applications, where efficient power coupling between the radio frequency (RF) source and the plasma is crucial. In practical CCP systems, impedance matching networks (IMNs) are employed to minimize power reflection. However, the presence of coaxial cables can significantly impact plasma impedance and matching performance. We develop a comprehensive simulation framework for the IMN design of CCPs, fully considering the effects of RF coaxial cables. The model self-consistently couples a distributed transmission line (TL) model, a lumped-element circuit model, and an electrostatic particle-in-cell model. This coupled model is used to investigate the impact of coaxial cables on matching performance under various discharge conditions and cable configurations. The simulation results indicate that the optimal power transmission efficiency was achieved after 6 matching iterations. The power coupled to the CCP increased from 2.7 W before matching to 180.9 W, and the reflection coefficient ultimately decreased to 0.003. The results also reveal that neglecting the cables will lead to a decrease in the power dissipated in the CCP. The proposed method demonstrates effectiveness in achieving impedance matching for different gas pressures (75–300 mTorr) and cable lengths. It can be concluded that the matching speed is faster for an appropriate cable length. This work provides valuable insights into the role of TLs in CCP impedance matching and offers a practical tool for optimizing power delivery in realistic CCP systems with RF coaxial cables. [ABSTRACT FROM AUTHOR]

Conductor breakage with ice load is one of the major threats to the safe operation of transmission lines. The ice load increases the unbalanced longitudinal tension, leading to failure of tower members and even progressive collapse of the transmission line. This paper proposes an autotriggered anticollapse fusing hardware (AAFH), designed to reduce the unbalanced longitudinal tension caused by conductor breakage. When the longitudinal unbalanced tension of the transmission line exceeds the threshold of the AAFH, the fused part is destroyed, and the AAFH is elongated to reduce the longitudinal unbalanced tension. First, the construction and working mechanism of the device are introduced, and a numerical model of the transmission line–AAFH system is established to verify its effectiveness. Then, a parameter determination method for unbalanced tension in the tower‐line system subjected to conductor breakage is proposed. In addition, the control performance of the device is studied. The results show that AAFH can effectively reduce the unbalanced tension induced by conductor breakage. The proposed method can predict the unbalanced tension of transmission lines, with an error within 10%. The greater the length of vertical/horizontal elongation, the better the protective effect. From a safety perspective, the AAFH should be designed according to the actual transmission line parameters to achieve an ideal control effect. [ABSTRACT FROM AUTHOR]

In order to investigate the characteristics and typical influential factors of wildfires caused by accidental faults in high-voltage transmission lines, a bespoke platform was constructed for the purpose of conducting simulation experiments. Discharge and ignition experiments were conducted on a variety of substrates, including kidney fern fragments, cedar needle fragments, poplar sawdust, and eucalyptus leaf fragments, to investigate the effects of different gaps on the initiation and propagation of wildfires. The results demonstrate that the discharge-inducing ignition stages can be succinctly summarized as "two phases and two points" (the discharge induction period, the gap breakdown point, the arc induction period and the fault removal point) when a suitable gap is maintained between the simulated falling lines and the vegetation surface. In the event of direct contact, the removal of the fault is not possible. The potential for ignition of the aforementioned vegetation types by the discharge is as follows: cedar needles > eucalyptus leaf fragments ≈ poplar sawdust > kidney fern fragments. As the water content increases, eucalyptus leaf fragments can still be ignited, and the breakdown voltages required for discharge-inducing ignitions gradually decrease. In the case of different forest ground vegetation types, when ignited by the discharge between the falling lines and the vegetation under conditions of proper gap and moisture content, the resulting ignition and sustained flames will promote the formation of streamer channels and further aggravate the discharges and burning processes, potentially leading to the ignition of a wildfire. [ABSTRACT FROM AUTHOR]

Point cloud semantic segmentation is crucial for identifying and analyzing transmission lines. Due to the number of point clouds being huge, complex scenes, and unbalanced sample proportion, the mainstream machine learning methods of point cloud segmentation cannot provide high efficiency and accuracy when extending to transmission line scenes. This paper proposes a filter-assisted airborne point cloud semantic segmentation for transmission lines. First, a large number of ground point clouds is identified by introducing the well-developed cloth simulation filter to alleviate the impact of the imbalance of the target object proportion on the classifier's performance. The multi-dimensional features are then defined, and the classification model is trained to achieve the multi-element semantic segmentation of the transmission line scene. The experimental results and analysis indicate that the proposed filter-assisted algorithm can significantly improve the semantic segmentation performance of the transmission line point cloud, enhancing both the point cloud segmentation efficiency and accuracy by more than 25.46% and 3.15%, respectively. The filter-assisted point cloud semantic segmentation method reduces the volume of sample data, the number of sample classes, and the sample imbalance index in power line scenarios to a certain extent, thereby improving the classification accuracy of classifiers and reducing time consumption. This research holds significant theoretical reference value and engineering application potential for scene reconstruction and intelligent understanding of airborne laser point cloud transmission lines. [ABSTRACT FROM AUTHOR]

With the rapid development of embroidered electronics, the research on microstrip lines as important connecting devices is crucial. Although the proposed research has explored the influence of embroidery parameters on some electrical properties of embroidered electronics, it is difficult to design and fabricate embroidered microstrip lines (EMLs) with specific transmission performance based on existing research. Therefore, in order to better design and fabricate EMLs with good transmission performance, the effect of embroidery parameters on their transmission performance needs further exploration. In this work, relevant models to simulate their transmission performance was proposed. Firstly, considering the cases where the conductive yarn is only distributed on the fabric surface as well as in both the surface and the thickness direction of the fabric, two geometry models are constructed to simulate the path of the conductive yarn in the embroidered conductor strip. Based on this, the transmission performances of the EML are simulated and analyzed. Then, two different sets of embroidered conductor strips were manufactured to fabricate microstrip line, and their transmission performance was tested to verify the rationality and accuracy of the above simulation model. Finally, based on simulation analysis and experimental testing, the influence of stitch spacing and stitch length on the transmission performance of EMLs are investigated, and the prediction bias of the models is analyzed. It is believed that the proposed model will provide a theoretical basis for the design of EMLs. [ABSTRACT FROM AUTHOR]

In modern power systems, there is a growing need for protection techniques that are fast, precise, and stable to enhance the reliability and stability of power networks. This paper introduces an advanced technique for fault direction estimation that addresses key limitations in existing methods. Our proposed algorithm eliminates the dead‐zone issue, effectively handling both forward and reverse faults regardless of their location, including faults near relay regions. Using genetic programing (GP), we derive a closed‐form equation to drive the fault direction estimation algorithm, ensuring accurate and reliable results. The algorithm demonstrates exceptional speed and reliability in fault detection and direction estimation, even under challenging conditions such as current transformer (CT) saturation, power swing, and source strength variations. It is also insensitive to fault resistance and fault inception angle, increasing its robustness. A notable advantage of this algorithm is its efficiency at low sampling frequencies, making it highly adaptable for industrial applications and compatible with modern software and hardware requirements. We validated the algorithm through simulations on the electromagnetic transients program (EMTP‐RV) platform and with field data from the Manesht 230 kV substation in Ilam province, Iran. The results confirm the algorithm's impressive speed and high reliability. [ABSTRACT FROM AUTHOR]

Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system's protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems. [ABSTRACT FROM AUTHOR]

Protection of critical components in the power sector especially the transmission line system is very important to minimize loss of revenue, resources, and productivity. Improvement of power transfer capacity and voltage profile in smart the power grid is achieved by Series Compensation (SC). However, it poses many serious issues in the protection of compensated transmission lines. The majority of faults occurring in transmission lines are transient in nature for which service continuity can be regained by an auto-reclosing scheme. An efficient auto-reclosing scheme that can provide adaptive protection with reliable operation is required to prevent transient and permanent faults in the series compensated transmission line (SCTL). This work highlights the emulation of an Internet of Things (IoT) aided adaptive auto-reclosing scheme in association with an ESP32 controller that provides adaptive dead time control. The reclosing instance has been identified by means of a synchro-check relay. A real-time IoT-aided auto-reclosing scheme has been realized using the ESP32 controller that can generate a trip/reclosing signal instantaneously using the concepts of edge computing. The projected system has been also corroborated by simulating the modeled smart power grid network with wide variations in fault context in PSCAD. The validation of the set of rules is done in MATLAB software for the proposed auto-reclosing scheme. The extracted test outcomes of the simulation and its laboratory hardware implementation indicate an appreciable reduction in dead time which reflects the efficacy of the developed protection scheme. [ABSTRACT FROM AUTHOR]

As more and more transmission lines need to pass through areas with heavy icing, the problem of transmission line faults caused by ice and snow disasters frequently occurs. Existing ice coverage monitoring methods have defects such as the use of a single monitoring type, low accuracy of monitoring results, and an inability to obtain ice coverage data over time. Therefore, this study proposes a new algorithm for detecting the icing status of transmission lines. The algorithm uses two-dimensional multifractal detrended fluctuation analysis (2D MF-DFA) to determine the optimal sliding-window size and wave function and accurately segment and extract local feature areas. Based on the local Hurst exponent ( L h (z) ) and the power-law relationship between the fluctuation function and the scale at multiple continuous scales, the ice-covered area of a transmission conductor was accurately detected. By analyzing and calculating the key target pixels, the icing thickness was accurately measured, achieving accurate detection of the icing status of the transmission lines. The experimental results show that this method can accurately detect ice-covered areas and the icing thickness of transmission lines under various working conditions, providing a strong guarantee for the safe and reliable operation of transmission lines under severe weather conditions. [ABSTRACT FROM AUTHOR]

Detecting defects in aerial images of grading rings collected by drones poses challenges due to the structural similarity between normal and defective samples. The small visual differences make it hard to distinguish defects and extract key features. Additionally, critical defect features often become lost during feature fusion. To address these issues, this paper uses YOLOv8 as the baseline model and proposes an improved YOLOv8-based method for detecting grading ring defects in transmission lines. Our approach first integrates the CloAttention and C2f modules into the feature extraction network, enhancing the model's ability to capture and identify defect features in grading rings. Additionally, we incorporate CARAFE into the feature fusion network to replace the original upsampling module, effectively reducing the loss of critical defect information during the fusion process. Experimental results demonstrate that our method achieves an average detection accuracy of 67.6% for grading ring defects, marking a 6.8% improvement over the baseline model. This improvement significantly enhances the effectiveness of defect detection in transmission line grading rings. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Given the increasing importance of cascading trip prevention, this study presents the pumped storage characteristics to cooperate with thermal units and further ensure the safety of grid operation. The proposed model comprises two phases: the first phase is the day-ahead decision-making phase, which synergistically optimizes thermal units and pumped storage to improve the flexibility of grid operation; the second phase is the intra-day adjustment phase considering the uncertainty of the wind-photovoltaic and transmission line failure outage to enhance the robustness. Furthermore, this model employs a column and constraint generation algorithm to solve the two-phase, three-layer optimization problem, thereby minimizing the adjustment cost under the riskiest scenarios. The experimental results show that adding pumped storage reduces the total system operating cost by 3.99%, the renewable energy abandonment rate to 0.65%, and the thermal power unit output standard deviation to 74.13 MW. [ABSTRACT FROM AUTHOR]

With the explosive growth of electricity consumption, the demand for electricity by electricity users is increasing. As a core component of power supply, the safe and stable operation of transmission lines plays an important role in the normal operation of the entire power system. However, traditional monitoring methods for transmission line operation status face challenges such as limited accuracy, lack of real-time feedback, and high operational costs. In this paper, the Firefly algorithm is used to monitor the running status of transmission lines. Through synchronous testing with the traditional particle swarm optimization algorithm, it is found that the average accuracy of the Firefly algorithm in voltage and current measurement is improved to 93.13% and 93.66% respectively, which is better than the traditional algorithm. Firefly algorithm shows high precision in various power equipment monitoring, the average monitoring accuracy is 95.62% and 93.06%, respectively, which proves that it has stronger performance in transmission line monitoring and can achieve more stringent monitoring requirements. Through the comparison experiment of the algorithm, it proved that the Firefly algorithm had a strong performance in the transmission line operation status monitoring, and could more accurately identify the transmission line fault, which provided a new idea and new method for the safe operation status monitoring of transmission lines. [ABSTRACT FROM AUTHOR]

Ice-covered transmission lines seriously affect the stable operation of the power system. Deploying a recognition network for measuring the ice thickness on transmission lines within a deicing robot, and controlling the robot to perform resonant deicing, is an effective solution. In order to solve the problem that the existing recognition network is not suitable for an edge device, an ice-thickness recognition network for transmission lines based on efficient dynamic perception (EDPNet) is proposed. Firstly, a lightweight multidimensional recombination convolution (LMRC) is designed to split the ordinary convolution for lightweight design and extract feature information of different scales for reorganization. Then, a lightweight deep fusion module (LDFM) is designed, which combines the attention mechanism with different features to enhance the information interaction between the encoder and decoder. Then, a new dynamic loss function is adopted in the training process to guide the model to perform refined detection of ice-covered boundaries. Finally, we count the ice pixels and calculate the ice thickness. The model is deployed on an OrangePi5 Plus edge computing board. Compared with the baseline model, the maximum ice-thickness detection error is 4.2%, the model parameters are reduced by 86.1%, and the detection speed is increased by 74.6%. Experimental results show that EDPNet can efficiently complete the task of identifying ice-covered transmission lines and has certain engineering application value. [ABSTRACT FROM AUTHOR]

Power system interruptions, though brief, carry significant costs and adverse consequences. Ensuring a reliable power supply necessitates accurate fault detection and swift resolution through fault classification and localization. The existing individual and joint approaches for fault classification and localization involve comprehensive expert-guided feature engineering and pre-processing algorithms depending on the selected classifier. This research proposes a fully automated, integrated multi-task deep learning-based framework called the Fault-MTL (multi-task learning) model for performing fault classification and localization at the same time. The multi-task feature interdependency accelerates the conduction of tasks with better performance. To the best of our knowledge, this is the first-of-its-kind study where the multi-task learning (MTL)-based deep model is developed for power system studies. To evaluate the model, we tested it on three state-of-the-art transmission line topologies, each with various initial conditions and simulated fault data variations in terms of fault type, distance, inception angle, and resistance. The tenfold cross-validation is used to measure the performance. The simulation experiments exhibit an exemplary performance by attaining the perfect validation in fault classification and the minimal mean average error of 1.59% in fault localization, across all three adapted topologies. For investigating the feasibility of the model, the ablation studies including the effect of noise, comparison with the existing state-of-the-art methods and the performance comparison of the work with the studies from the literature are performed in individual and combined simulation experiments of fault classification and fault localization. [ABSTRACT FROM AUTHOR]

This study introduces a compact wearable metasurface transmission line operating at 2.45 GHz, utilizing spoof surface plasmon polariton (SSPP). The design incorporates a periodic arrangement of hook unit cells and a tapering structure to enhance coupling efficiency between the microstrip feed line and the metasurface transmission line. For its implementation, a dielectric substrate composed entirely of cotton denim jeans is employed, along with e-textile conductive traces and a ground plane. Electromagnetic wave propagation along the transmission line is simulated, and the primary wave mode and associated propagation characteristics are analyzed. Both simulation and measurement results confirm the successful performance of the metasurface transmission line for on-body signal transmissions. [ABSTRACT FROM AUTHOR]

The electric field of transmission lines has serious negative impacts on residents' production and life with the expansion of high voltage engineering. In order to study the influence of trees on the electric field of ultra-high voltage transmission lines, this paper conducted three-dimensional simulation calculations of the power frequency electric field of transmission lines based on the tree quantity and distribution. Firstly, in order to study the pattern of electric field strength distribution in transmission lines, the electric field strengths of transmission lines of different voltage levels were compared; the maximum-power-frequency electric field intensity of ultra-high voltage transmission lines occurs below the edge conductor. Secondly, by changing the number of trees, it was concluded that the electric field strength below the edge conductor gradually decreases with the number of trees. Finally, the maximum electric field strength value at 1.5 m below the edge conductor and the width of the transmission corridor decreased by changing the layout of the trees. The results show that studying the impact of a tree's electromagnetic parameters on the power frequency electric field strength under transmission lines can help reduce the electric field strength and decrease the width of transmission corridors, which is of great significance for line design and cost savings. [ABSTRACT FROM AUTHOR]

The fault diagnosis on a transmission line based on the characteristics of the power spectral entropy is proposed in this article. The data preprocessing for the experimental measurement is also introduced using the EEMD. The EEMD is used to preprocess experimental measurements, which are nonlinear and non-stationary fault signals, to overcome the mode mixing. This study focuses on the fault location detection of transmission lines during faults. The proposed method is adopted for different fault types through simulation under the fault point by collecting current and voltage signals at a distance from the fault point. An analysis and comprehensive evaluation of three-phase measured current and voltage signals at distinct fault locations is conducted. The form and position of the fault are distinguished directly and effectively, thereby significantly improving the transmission line efficiency and accuracy of fault diagnosis. [ABSTRACT FROM AUTHOR]

At present, the identification of icing status of transmission line conductors is mainly manual. Although the mainstream method based on intelligent vision and UAV can solve the manual problem, the specific situation of conductor icing cannot be further mastered. Based on the information fusion of optical fiber sensor and continuous wavelet decomposition, the online sensing method of transmission line icing is studied. This method uses the transmission line conductor vibration and stress signal acquisition method based on distributed optical sensing technology. After the transmission line conductor vibration and stress signals are collected online by multiple optical fiber sensors, the fusion model based on Elman neural network is used to fuse the multiple optical fiber sensing information, and then the spectrum feature extraction method of optical fiber sensing signal based on continuous wavelet decomposition is used. The spectral characteristics of fused optical fiber sensing signals are extracted as diagnostic samples of the online perception method of conductor icing based on improved logical regression, and the binary classification method is used to identify whether there is icing problem on transmission line conductors online. In the experiment, this method can accurately perceive the online perception results of rime type icing, mixed rime type icing, wet snow type icing, and has the function of accurately sensing the icing status of transmission line conductors. [ABSTRACT FROM AUTHOR]

Traditional fault-location methods applied to a double-circuit transmission line are usually implemented according to an abc-domain or a sequence network equivalent circuit that also varies with fault type. However, this dependence of those fault location algorithms from the line parameters suffers from inaccuracy injected under varying environmental conditions altering the parameters of the double-circuit transmission line. Herein, an Extreme Learning Machine-based line parameter-independent fault location method is suggested that can learn the nonlinear relationship between the voltages, currents measured, and fault locations with high accuracy. In the presented paper, the proposed method is simulated for different fault types at random distances in a power grid containing a double-circuit transmission line. The simulated data are then utilized for training the intelligent fault location system. Further, different distances and resistances fault locations are estimated to check the accuracy of the proposed approach. The obtained results are compared with the results of two other intelligent fault detection approaches, such as ANN and SVM and better accuracy and reliability are shown in ELM. The outputs of these tests show considerable improvements in the proposed technique of fault location on a double circuit transmission line under different environmental conditions. [ABSTRACT FROM AUTHOR]

Severe ice accretion on transmission lines can disrupt electrical grids and compromise the stability of power systems. Consequently, precise prediction of ice coating on transmission lines is vital for guiding their operation and maintenance. Traditional single-model icing prediction methods often exhibit limited accuracy under varying environmental conditions and fail to yield highly accurate predictions. We propose a multi-scenario, two-stage adaptive ensemble strategy (MTAES) for ice coating prediction to address this issue. A combined clustering approach is employed to refine the division of ice weather scenarios, segmenting historical samples into multiple scenarios. Within each scenario, the bagging approach generates multiple training subsets, with the extreme learning machine (ELM) used to build diverse models. Subsequently, a two-stage adaptive weight allocation mechanism is introduced. This mechanism calculates the distance from the scenario cluster centers and the prediction error of similar samples in the validation set for each test sample. Weights are dynamically allocated based on these data, leading to the final output results through an adaptive ensemble from the base model repository. The experimental results show that the model is significantly better than traditional models in predicting ice thickness. Key indicators of RMSE, MAE, and  R 2  reach 0.675, 0.522, and 83.2%, respectively, verifying the effectiveness of multi-scene partitioning and adaptive weighting methods in improving the accuracy of ice cover prediction. [ABSTRACT FROM AUTHOR]

This article presents a buck converter in which the inductor has been replaced by a transmission line. This approach would be practically conceivable if the power supply and load had a greater spatial distance. Alternatively, the model derived in this way could also be regarded as an intermediate model in order to replace a power coil via discretization with a larger number of smaller coils and capacitors. In the time domain, this new converter can be described by a system of coupled partial and ordinary differential equations. In the frequency domain, a transcendental transfer function is obtained. For comparison with an equivalently parameterized conventional converter, Padé approximants are derived. A linear controller is designed for the converter topology under consideration. [ABSTRACT FROM AUTHOR]

Due to extreme weather conditions and complex geographical features, the environments around power lines in forest areas have a high risk of wildfires. Once a wildfire occurs, it causes severe damage to the forest ecosystem. Monitoring wildfires around power lines in forested regions through deep learning can reduce the harm of wildfires to natural environments. To address the challenges of wildfire detection around power lines in forested areas, such as interference from complex environments, difficulty detecting small target objects, and high model complexity, a lightweight wildfire detection model based on the improved YOLOv8 is proposed. Firstly, we enhanced the image-feature-extraction capability using a novel feature-extraction network, GS-HGNetV2, and replaced the conventional convolutions with a Ghost Convolution (GhostConv) to reduce the model parameters. Secondly, the use of the RepViTBlock to replace the original Bottleneck in C2f enhanced the model's feature-fusion capability, thereby improving the recognition accuracy for small target objects. Lastly, we designed a Resource-friendly Convolutional Detection Head (RCD), which reduces the model complexity while maintaining accuracy by sharing the parameters. The model's performance was validated using a dataset of 11,280 images created by merging a custom dataset with the D-Fire data for monitoring wildfires near power lines. In comparison to YOLOv8, our model saw an improvement of 3.1% in the recall rate and 1.1% in the average precision. Simultaneously, the number of parameters and computational complexity decreased by 54.86% and 39.16%, respectively. The model is more appropriate for deployment on edge devices with limited computational power. [ABSTRACT FROM AUTHOR]

Aimed at the interconnection between coaxial transmission line and substrate‐integrated waveguide (SIW), a wideband coaxial‐to‐SIW transition was proposed and applied in a 1‐to‐4 radial power divider. First of all, a fan‐shaped region was designed on the end of a SIW transmission line. Then, a coaxial transmission line was inserted into this region. To improve the impedance matching at the coaxial port, a feeding ring was designed on the back of the transition where the coaxial line was inserted. Next, the transition was applied in a 1‐to‐4 radial power divider to realize an equal power division. Finally, to validate the correctness of the designs, prototypes were fabricated and measured. For the coaxial‐to‐SIW transition, measured reflection coefficient |S11| was less than −15 dB from 7.84 to 12.18 GHz. Transmission coefficient |S21| was between −0.52 and −0.24 dB. For the 1‐to‐4 radial divider, measured |S11| was less than −15 dB from 7.91 to 12.4 GHz, and the transmission coefficient was around −6.9 dB. Measurement and simulation agreed well with each other. Therefore, the proposed design can be used in coaxial‐to‐SIW interconnection scenarios. [ABSTRACT FROM AUTHOR]

This work presents two Wilkinson power dividers (WPDs) using multi-layer pseudo coplanar waveguide (PCPW) structures. The PCPW-based WPDs were designed, implemented, and verified in a standard 180 nm CMOS process. The proposed PCPW features high slow-wave and low-loss performances compared to other common transmission lines. The two WPDs are based on the same PCPW structure parameters in terms of line width, spacing, and used metal layers. One WPD was realized in a straight PCPW-based layout, and the other WPD was realized in a meandered PCPW-based layout. Both the two WPDs worked up to V-band frequencies, as expected, which also demonstrates that the PCPW guiding structure is less susceptible to the effects of meanderings on the propagation constant and characteristic impedance. The meandered design shows that the measured insertion losses were about 5.1 dB, and its return losses were better than 17.5 dB at 60 GHz. In addition, its isolation, amplitude imbalance, and phase imbalance were 18.5 dB, 0.03 dB, and 0.4°, respectively. The core area was merely 0.2 mm × 0.23 mm, or 1.8 × 10−3λo2. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Identifying the fault location in the transmission lines and fixing the problem in the shortest possible time is of particular importance to reduce blackouts and increase the reliability of the network. One of these fault location methods is the impedance- based method. The precise values of transmission line parameters and the Thevenin impedance of the network are unknown due to various issues such as temperature change and network structure, which can affect the accuracy of this method in locating the fault in both uncompensated and compensated transmission lines. Therefore, in this article, the proposed method is an intelligent impedance-based method using the variable neighborhood search algorithm, regarding the uncertainty in the line parameters and the Thevenin impedance of the network in locating single- phase-to-ground faults. In the proposed method, only one-end-current data is used to estimate the location of the fault, which does not require a telecommunication platform and results in increasing the reliability of the location method. The simulation results show that the method has high accuracy in fault location in transmission lines even though the line parameters and Thevenin impedance of the network are unknown. Moreover, the resistance, the angle and the location of the fault, and the level of compensation do not have a significant effect on the accuracy of the fault location. [ABSTRACT FROM AUTHOR]

In a wide range of current changes, it is difficult for current transformer to carry out steady energy extraction, because the excitation characteristics of current transformer are interfered by load impedance. To solve this problem, a dynamic impedance control strategy of voltage source converter is proposed. The method dynamically and continuously adjusts the secondary impedance according to the different states of the primary current, which not only meets the requirement of load power consumption under the condition of low current, but also reduces the influence of the magnetic saturation problem of the core. The principle of energy collection is analyzed, and the equivalent model of current transformer and circuit is established. The relationship between excitation characteristics and impedance parameters is discussed empirically-, which lays a foundation for the construction of the final control strategy. The experimental results show that, first, without capacitive impedance control, the effective value of ct induced voltage is 37.313V, and the corresponding energy collection power is 1.39W. Under the capacitive impedance control, the RMS value of ct induced voltage increases to 72.868V and the energy collection power reaches 5.31W. It can be seen that when the capacitive reactance state is changed, the RMS value of induced voltage and energy collection power are significantly different. Second, the output current of the strong current generator is adjusted to 70a, and when the current increases, the system enters the magnetic saturation state. At this time, the positive and negative half waves of the voltage and current on the CT secondary side produce high amplitude spike pulse waves, which is very dangerous to the insulation of the CT winding and the safety of the load equipment. Under the same conditions, after the induction impedance control is added to the CT core, the magnetic saturation state is relieved, and the waveform changes from peak wave to sine wave, the energy taking system returns to normal working state, the DC voltage output is normal, and the system power supply is stable. Third, the output current of the strong current generator continues to increase to 80a, with the increase of the primary current, the system waveform state remains stable, and the induced voltage and DC output voltage remain constant near the preset reference value. It can be concluded that the magnetic induction intensity of the core will be positively affected by the capacitive impedance operation, and the energy supply effect of the transformer will be increased. The operating characteristics of inductance impedance can reduce the magnetic induction intensity of the iron core, thus inhibiting the saturation of the iron core under high current operating conditions and maintaining stable power output. [ABSTRACT FROM AUTHOR]

Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and rainwater. This paper presents an integrated numerical simulation framework based on mesoscale WRF model, multiphase CFD model and FEM model to analyze the motions of a transmission line subjected to coupled wind and rain loads during typhoon events. A full-scale transmission line in Zhoushan Island is employed to demonstrate the effectiveness of the proposed framework by simulating typhoon evolution in terms of wind fields and rainfall, solving the coupled wind and rain fields around the conductor and predicting the dynamic responses of the transmission line during Super Typhoon Lekima in 2019. The results show that the horizontal displacements of the transmission line under the joint actions of wind and rain increase approximately 17–18% compared to those of wind loads only. It is important to consider the coupled effects of wind-driven rain on conductors in the design of transmission lines under typhoon conditions. [ABSTRACT FROM AUTHOR]

Strong winds can lead to more complex ice shedding oscillation processes for overhead conductors, inducing flashovers, strand breakages and other accidents. This study analyzes the aerodynamic parameters of several typical icing features and establishes a numerical model for ice shedding on overhead conductors under strong wind conditions. The results show that for the same amount of icing, the resistance and lift force on the conductor changes with ice shape, wind attack angle and wind speed, which has a significant effect on the ice shedding jumping process. When the wind attack angle approaches 180°, the airflow resistance of the fan-shaped and D-shaped icing conductors significantly increases. And in the process of ice shedding response of transmission lines, the lateral amplitude may exceed 20 m, which increase the discharge risk of horizontally arranged conductors. Moreover, for the significant lateral oscillation of conductors by ice shedding under strong wind, the maximum horizontal displacement is approximately 1.6 times the difference in lateral position before and after ice shedding. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Exact Sciences is the property of Master Editora and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High-frequency transmission is limited to the skin depth in metals. Because poor conductivity cannot be compensated for by increasing the conductor thickness as with DC, optimal transport properties are prerequisites for radio frequency (RF) use. Structural and chemical analyses of transmission lines printed using a traditional ink consisting of Ag nanoflakes in a dispersing phase revealed that optimized thermal treatments yielded thorough burnout of the binder, significant grain growth, elimination of the pore volume, and electrical responses that were comparable to values obtained for thermally evaporated, fully dense Ag controls. Specifically, a low DC resistivity of 2.3 μΩ·cm (1.4× bulk Ag) and RF transmission coefficients of 0.87 and 0.75 at 5 GHz and 10 GHz, respectively, were measured in the nanoflake Ag prints. Conversely, in transmission lines printed from a metal-organic decomposition ink, residual chemical contamination impeded diffusion and densification, yielding greater porosity, small grains that are pinned, and a degraded RF response. Reasonably good porosity approximations were obtained from a model based on percolation theory. The results indicate that contaminants at interfaces and pore surfaces impede diffusion, pore elimination, and full densification, and further, alter carrier dynamics and degrade RF response. [ABSTRACT FROM AUTHOR]

The planning, scheduling and operation decisions of the power grid depend on the calculation of the simulation model. Parameter errors in the grid model can lead to deviations between simulation calculations and actual grid operation. The strategy based on incorrect calculation data will lead to power outages in the actual power grid, which may cause significant economic losses and personal safety accidents. For the safe operation of power grid, a method for locating the wrong parameters of transmission line based on sensitivity analysis (SA) and deep residual network (DRN) is proposed. By calculating the sensitivity of apparent power to parameter error of each line in the power grid, the propagation characteristics of power flow error are analyzed quantitatively. An error region segmentation method is proposed to reduce the search range of error parameters from large-scale power grids to local networks which can reduce the computational complexity of the search algorithm, and increase accuracy. An error transmission line index for local power grids is proposed to identify error source in local power grids. Then, the specific wrong parameters are identified through the DRN. It can intelligently identify the error parameters from multiple parameters of the error transmission line. The calculation results of the 300-bus system verify the correctness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Various new conductive materials with exceptional properties are utilized for the preparation of electronic devices. Achieving ultrahigh conductivity is crucial to attain excellent electrical performance. However, there is a lack of systematic research on the impact of conductor material thickness on device performance. Here, we investigate the effect of conductor thickness on power transmission and radiation in radio-frequency (RF) and microwave electronics based on MXene nanosheets material transmission lines and antennas. The MXene transmission line with thickness above the skin depth exhibits a good transmission coefficient of approximately −3 dB, and the realized gain of MXene antennas exceeds 2 dBi. Additionally, the signal transmission strength of MXene antenna with thickness above the skin depth is higher than 5-µm MXene antenna approximately 5.5 dB. Transmission lines and antennas made from MXene materials with thickness above the skin depth exhibit stable and reliable performance, which has significant implications for obtaining high-performance RF and microwave electronics based on new conductive materials. [ABSTRACT FROM AUTHOR]

After a transmission line is covered by ice in winter, ice-shedding and vibration occurs under special meteorological and external dynamic conditions, which leads to intense transmission line shaking. Transmission line ice-shedding and vibration often cause line flashover trips and outages. In January 2018, three 500 kV transmission lines, namely, the 500 kV Guanli line, the 500 kV Dushan line, and the 500 kV Guanqiao line, tripped and cut off due to ice-shedding and vibration in Anhui province, seriously threatening the safe operation of a large power grid. Current studies mainly focus on analyzing the influence factors and characteristics of line ice-shedding and investigating suppression measures, but they only analyze the correlation between each influencing factor and icing or shedding, and do not consider the coupling effects between multiple factors. In this paper, the key influencing factors and the probability distribution of transmission line ice-shedding were analyzed, and a multiple-factor coupling fault probability calculation model of line ice-shedding based on Copula function was proposed. The fault probability was calculated directly by considering multiple influence factors at the same time, which effectively overcame the error caused by multi-factor transformation in fuzzy membership degree and other methods. It provided an important decision-making basis for preventing and controlling transmission line ice-shedding faults. [ABSTRACT FROM AUTHOR]

Due to the influence of complex control, the short circuit current provided by wind power generation units will exhibit new characteristics, such as limited amplitude and controlled phase, affecting the performance of traditional protections. This paper analyzes the work principle and defects of one kind of multiphase compensated distance protection. Drawing on the idea of fault identification by multiphase compensated distance protection, novel pilot protection using the compensated voltage on both sides is proposed. The proposed method has a strong anti-fault resistance ability. The test results indicate that the proposed method can distinguish the internal faults and external faults reliably, unaffected by the new characteristics of short circuit current. [ABSTRACT FROM AUTHOR]

We proposed a newly devised method for measuring the complex permittivity of high-loss materials at the millimeter wave frequency range. In this paper, we employed a geometrical gap between samples, which allows us to evaluate the complex permittivity of high-loss materials. By introducing this gap, we obtain a characteristic shape of the transmittance, which enables us to fit the data and obtain a more precise complex permittivity value. The proposed method has been supported by experimental results with a very low error bar compared to the existing fundamental transmission line method. We measured diverse samples including alumina, Teflon, Vero family, and AlN-SiC, and confirmed that the proposed gap method achieves much higher precision than the existing method by 3 to over 10 times. Particularly for high-loss materials like the AlN-SiC sample, the average error of the relative permittivity evaluated by the existing transmission line method is 2.05% whereas the error by the proposed method is 0.05%. Moreover, the average error of the loss tangent measured by the existing method and the suggested method is 1.85% and 0.3%, respectively, which confirms the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

As an important part of the power system, it is necessary to ensure the safe and stable operation of transmission lines. Due to long-term exposure to the outdoors, the lines face many insecurity factors, and foreign object intrusion is one of them. Traditional foreign object (bird's nest, kite, balloon, trash bag) detection algorithms suffer from low efficiency, poor accuracy, and small coverage, etc. To address the above problems, this paper introduces the RCDAM-Net. In order to prevent feature loss or useful feature compression, the RevCol (Reversible Column Networks) is used as the backbone network to ensure that the total information remains unchanged during feature decoupling. DySnakeConv (Dynamic Snake Convolution) is adopted and embedded into the C2f structure, which is named C2D and integrates low-level features and high-level features. Compared to the original BottleNeck structure of C2f, the DySnakeConv enhances the feature extraction ability for elongated and weak targets. In addition, MPDIoU (Maximum Performance Diagonal Intersection over Union) is used to improve the regression performance of model bounding boxes, solving the problem of predicted bounding boxes having the same aspect ratio as true bounding boxes, but with different values. Further, we adopt Decoupled Head for detection and add additional auxiliary training heads to improve the detection accuracy of the model. The experimental results show that the model achieves mAP50, Precision, and Recall of 97.98%, 98.15%, and 95.16% on the transmission tower line foreign object dataset, which is better to existing multi-target detection algorithms. [ABSTRACT FROM AUTHOR]

Accurate fault location on transmission lines is paramount for ensuring the reliable and efficient operation of the electricity grid, which underpins every aspect of modern society. Existing fault localization methods for transmission lines often face shortcomings, particularly in scenarios involving multi-terminal transmission lines, where complexities arise due to dispersed generations and intricate network configurations. Traditional approaches may struggle to provide accurate fault localization, impacting the reliability and efficiency of the electricity grid. Research provide a unique fault localization technique in this article depends on Phasor Measurement Units (PMUs) and Bidirectional Gradient Boost Random Forest (BDGB-RF) machine learning technique to address these challenges. The proposed method offers several advantages over traditional methods, including enhanced accuracy and efficiency. By leveraging PMU data and BDGB-RF, the method provides a two-phase fault localization strategy, incorporating fault line selection based on nodal current imbalance and subsequent fault distance determination. Simulation results demonstrate the effectiveness of the proposed approach, achieving up to 97% accuracy in the majority of studied scenarios, even under different tapping configurations. The adoption of this approach could significantly impact the practices of experts in the field, facilitating more reliable fault detection and localization in complex transmission line networks. This, in turn, can contribute to the resilience and stability of power systems, ultimately improving grid reliability and minimizing downtime. [ABSTRACT FROM AUTHOR]