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The combination of the dark fiber in existing Optical Fiber Composite Overhead Ground Wire (OPGW) with Distributed Optical Fiber Sensing (DOFS) technology can be used to enable online monitoring and provide early warnings of anomalies in high-voltage transmission lines. Accurate mapping of the optical cable length to the geographic coordinates of actual towers is a key factor in achieving this goal. This paper discusses the principle of using a DOFS system for transmission line tower positioning and presents four available positioning features. To overcome the limitations of single physical parameter positioning, this paper presents a self-developed hybrid DOFS that simultaneously captures Rayleigh backscattering and Brillouin scattering signals. Several physical parameters, including temperature, strain, and vibration, are acquired synchronously. Through hybrid multi-parameter analysis, the rapid and accurate positioning of OPGW line towers is achieved. Experimental results have shown that the proposed method, based on the hybrid DOFS system, can locate up to 82 towers, while the traditional method could only identify 12. The hybrid system was able to complete 80% of the tension towers in 40 h. This paper presents a novel multi-parameter localization method that has the potential to significantly improve the efficiency and reliability of grid operation and maintenance. [ABSTRACT FROM AUTHOR]

Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network's ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks. [ABSTRACT FROM AUTHOR]

South Africa is currently experiencing an energy crisis because of a mismatch between energy supply and demand. Increasing energy demand necessitates the adequate operation of generation and transmission facilities to maintain the reliability of the power system. Transmission line compensation is used to increase the ability to transfer power, thereby enhancing system stability, voltage regulation, and reactive power balance. Also, in recent years, the introduction of renewable energy sources (RES) has proven to be effective in supporting the grid by providing additional energy. As a result, the dynamics of power systems have changed, and many developing nations are adopting the integration of renewable energy into the grid to increase the aspect ratio of the energy availability factor. While both techniques contribute to the grid's ability to meet energy demand, they frequently introduce technical challenges that affect the stability and protection of the systems. This paper provides a comprehensive review of the challenges introduced by transmission line compensation and the integration of renewable energy, as well as the various techniques proposed in the literature to address these issues. Different compensation techniques, including fault detection, classification, and location, for compensated and uncompensated transmission lines, including those connected to renewable energy sources, are reviewed. This paper then analyzes the adaptive distance protection schemes available in the literature to mitigate the impact of compensation/integration of RES into the grid. Based on the literature reviewed, it is essential for protection engineers to understand the dynamics introduced by network topology incorporating a combination of RES and heavily compensated transmission lines. [ABSTRACT FROM AUTHOR]

Due to the influence of many factors, distribution cables are often densely placed at the bottom of the cable trench. As a result, it is easy for distribution cables to become the thermal bottleneck of the whole transmission line. To address this dilemma, this paper establishes a finite element simulation model of a cable trench to analyze the hot spots of cables with different arrangements in the cable trench. Then, the model's accuracy is verified based on real temperature rise experiments. For an arrangement with overheating risk, the ampacity improvement method of filling the cable trench with high-thermal-conductivity material was proposed, and the ampacity improvement effect under different filling ratios was assessed. Finally, combined with the analysis of economic benefit and cost, the method of determining the optimal filling ratio was used, and the impact resistance of the cables under the impact of new energy load was analyzed. The results indicate that, for the case of the optimal filling ratio, the cables in the dense cable trench showed superior impact resistance. The investigations in this paper make significant contributions to the promotion of the maximum utilization of cables. [ABSTRACT FROM AUTHOR]

One of the key challenges in Multi-Spectral Automatic Diagnostic (MAD) robot design is the precise targeting of narrow-angle cameras on a specific part of the equipment. The paper shows that a low-cost MAD robot, whose navigation system is based on open-source ArduRover firmware and a pair of low-cost Ublox F9P GNSS receivers, can inspect the 8 × 4 degree ultraviolet camera bounding the targeting error within 0.5 degrees. To achieve this result, we propose a new targeting procedure that can be implemented without any modifications in ArduRover firmware and outperforms more expensive solutions based on LiDAR SLAM and UWB. This paper will be interesting to the developers of robotic systems for power equipment inspection because it proposes a simple and effective solution for MAD robots' camera targeting and provides the first quantitative analysis of the GNSS reception conditions during power equipment inspection. This analysis is based on the experimental results collected during the inspection of the overhead power transmission lines and equipment inspections on the open switchgear of different power plants. Moreover, it includes not only satellite, dilution of precision, and positioning/heading estimation accuracy but also the direct measurements of angular errors that could be achieved on operating power plants using GNSS-only camera targeting. [ABSTRACT FROM AUTHOR]

This paper proposes a novel methodology to improve stability in a transmission system under critical conditions of operation when additional loads that take the system to the verge of stability are placed in weak bus bars according to the fast voltage stability index (FVSI). This paper employs the Newton–Raphson method to calculate power flows accurately and, based on that information, correctly calculate the FVSI for every transmission line. First, the weakest transmission line is identified by considering N − 1 contingencies for the disconnection of transmission lines, and then all weak nodes associated with this transmission line are identified. Following this, critical scenarios generated by stochastically placed loads that will take the system to the verge of instability will be placed on the identified weak nodes. Then, the methodology will optimally size and place a single static VAR compensator SVC in the system to take the transmission system to the conditions before the additional loads are connected. Finally, the methodology will be validated by testing the system for critical contingencies when any transmission line associated with the weak nodes is disconnected. As a result, this paper's methodology found a single SVC that will improve the system's stability and voltage profiles to similar values when the additional loads are not connected and even before contingencies occur. The methodology is validated on three transmission systems: IEEE 14, 30, and 118 bus bars. [ABSTRACT FROM AUTHOR]

This paper presents the findings of the research aimed at developing computer models to determine the operating conditions in electric power systems (EPSs) feeding DC and AC railway substations. The object of the research is an EPS with a predominant traction load whose high-voltage power lines are connected to transformer and converter substations with 3 kV and 27.5 kV traction networks. The supply network includes 110 kV and 220 kV power lines. The EPS operating parameters are calculated based on the decomposition of the system into alternating and direct current segments. Calculations are performed for the fundamental frequency and high harmonic frequencies. The modeling technique is universal and can be used to determine the operating parameters and power quality indices for any configuration of an EPS and various designs of traction networks. With this technique, one can solve numerous additional problems, such as calculating the processes of ice melting in traction networks and power lines, determining electromagnetic field strengths, and assessing the heating of power line wires and catenary suspensions. The results obtained show that the voltages on the current collectors are within acceptable limits for all AC and DC electric locomotives. The levels of asymmetry on the 110 and 220 kV tires of traction substations (TP) do not exceed the normally permissible values. The values of the asymmetry coefficients for DC TP are tenths of a percent. With an increase in the size of traffic and in post-emergency conditions caused by the disconnection of communication between one of the support substations and the EPS, the asymmetry indicators on the 220 kV buses of AC substations may exceed the permissible limits. Phase-controlled reactive power sources can be used to reduce them. The analysis of the results of the determination of non-sinusoidal modes allows us to formulate the conclusion that the values of harmonic distortion go beyond the normative limits. Passive and active filters of higher harmonics can be used to normalize them. Calculations of thermal modes of traction transformers show that the temperatures of the most heated points do not exceed acceptable values. [ABSTRACT FROM AUTHOR]

The prevailing approach to the wind resistance design of transmission towers is rooted in the quasi-static method. However, this methodology faces criticism for neglecting tower-line coupling dynamics. Despite efforts to boost structural wind resilience, the research on tower failure mechanisms, especially under extreme winds considering tower-line coupling, is limited. To address this gap, the wind-induced dynamic failure modes of the transmission tower-line system are investigated in this paper. The consistent discrete random flow generation method is employed to simulate the fluctuating wind field for transmission lines. Incorporating the compressive buckling mode of angle steel, the plastic hinge model of the frame element is employed to simulate mechanical nonlinearity. A typical transmission tower-line system is concerned, with a finite element model established for a three-tower, four-line coupled configuration. The findings reveal that the wind-induced collapse of the transmission tower is directly triggered by the buckling failure of the compressed main members, with the vulnerable section located beneath the lower cross-arm. The transmission tower experiences bidirectional bending and compression instability under an unfavorable wind direction. In contrast, the traditional pushover collapse modes of the transmission tower cannot fully capture the characteristics of the collapse failure, mainly due to the ignorance of the transverse wind force action induced by the coupling effect. This research underscores the importance of incorporating lateral dynamic considerations into transmission tower designs and advocates for optimizing strategies to mitigate wind-induced collapse modes. [ABSTRACT FROM AUTHOR]

Industrial development and population growth have increased the need for higher-capacity power transmission lines. Aluminum conductor steel-supported (ACSS) conductors, a type of high-temperature low-sag (HTLS) conductor, are now widely used in new designs and reconductoring applications. ACSS conductors are preferred over traditional aluminum conductor steel-reinforced (ACSR) conductors due to their high strength, low sag, and excellent thermal stability. These attributes have garnered significant interest from researchers, engineers, and manufacturers. This paper provides a comprehensive review of the structure, properties, testing methods, and environmental behavior of ACSS conductors. [ABSTRACT FROM AUTHOR]

Definition: On 1 January 2001, the IEC 61000-3-2 regulation became effective. Since then, mitigating current harmonics has been essential to ensure that electronic equipment connected to single-phase power distribution lines conforms to electromagnetic compatibility directives. Today, high-quality rectification, commonly known as power factor correction (PFC), is a well-established technique widely adopted by the industry for powering various devices from the ac line. The topic has been studied by academia and industry since the mid-1980s; thus, an enormous amount of research has been published and countless solutions have been proposed since then. However, only a few of those solutions have encountered wide industrial usage. So, it is not the authors' intention to provide a comprehensive review, but to take stock of the most used PFC techniques from an industry perspective. This paper will review the power factor theory with non-sinusoidal currents, the practical and regulatory aspects of using PFC, and the most common industry solutions for power factor correction in equipment operated from the single-phase, public, low-voltage supply system, with a special focus on boost PFC pre-regulators, their control methods, design procedures, and issues. [ABSTRACT FROM AUTHOR]

Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images. [ABSTRACT FROM AUTHOR]

To address the frequent external damage incidents to transmission line corridors caused by construction machinery such as excavators and cranes, this paper constructs a dataset of external damage hazards in transmission line corridors and proposes a detection method based on YOLO-LSDW for these hazards. Firstly, by incorporating the concept of large separable kernel attention (LSKA), the spatial pyramid pooling layer is improved to enhance the information exchange between different feature levels, effectively reducing background interference on external damage hazard targets. Secondly, in the neck network, the traditional convolution is replaced with a ghost-shuffle convolution (GSConv) method, introducing a lightweight slim-neck feature fusion structure. This improves the extraction capability for small object features by fusing deep semantic information with shallow detail features, while also reducing the model's computational load and parameter count. Then, the original YOLOv8 head is replaced with a dynamic head, which combines scale, spatial, and task attention mechanisms to enhance the model's detection performance. Finally, the wise intersection over union (WIoU) loss function is adopted to optimize the model's convergence speed and detection performance. Evaluated on the self-constructed dataset of external damage hazards in transmission line corridors, the improved algorithm shows significant improvements in key metrics, with mAP@0.5 and mAP@0.5:0.95 increasing by 3.4% and 4.6%, respectively, compared to YOLOv8s. Additionally, the model's computational load and parameter count are reduced, and it maintains a high detection speed of 96.2 frames per second, meeting real-time detection requirements. [ABSTRACT FROM AUTHOR]

For microgrids in deep and remote sea areas, ocean currents are widely used as an emerging power generation resource. Implementing ES systems is crucial for smooth power output and grid stability. The stability of power output from sea current energy generation faces challenges due to speed fluctuations. Enhancing resilience requires addressing transmission line failures caused by extreme seabed conditions, and ensuring operational security. An ES configuration method considering line faults based on two-stage robust optimization is presented in this paper. First, in order to simultaneously consider planning and operation, a defender–attacker–defender (DAD) model was established. Additionally, the capacity, rated power, and charging/discharging power of ES during operation were jointly optimized through the column-and-constraint generation (C&CG) algorithm. In addition, the rationality and effectiveness of the proposed method were demonstrated through experiments on modified IEEE six-bus and fifty-seven-bus systems. The results show that a distributed ES configuration increased system resilience by 54.60% and reduced abandoned power rate by 57.06% compared with the situation without ES configuration. [ABSTRACT FROM AUTHOR]

In the power grid, inspection robots enhance operational efficiency and safety by inspecting power lines for information sharing and interaction. Edge computing improves computational efficiency by positioning resources close to the data source, supporting real-time fault detection and line monitoring. However, large data volumes and high latency pose challenges. Existing offloading strategies often neglect task divisibility and priority, resulting in low efficiency and poor system performance. This paper constructs a power grid inspection offloading scenario using Python 3.11.2 to study and improve various offloading strategies. Implementing a game-theory-based distributed computation offloading strategy, simulation analysis reveals issues with high latency and low resource utilization. To address these, an improved game-theory-based strategy is proposed, optimizing task allocation and priority settings. By integrating local and edge computing resources, resource utilization is enhanced, and latency is significantly reduced. Simulations show that the improved strategy lowers communication latency, enhances system performance, and increases resource utilization in the power grid inspection context, offering valuable insights for related research. [ABSTRACT FROM AUTHOR]

This paper introduces a new concept in transmission expansion planning based on unconventional lines, termed "smart transmission expansion planning". Traditionally, the domains of transmission expansion planning (TEP) and transmission line design are separate entities. TEP planners typically rely on the electrical specifications of a limited set of standard conventional line designs to evaluate planning scenarios, ultimately leading to the construction of the selected candidate line. In this context, it is noted that cost-effective scenarios often diverge from meeting the technical criteria of load flow analysis. To address this discrepancy, this paper proposes an alternative approach wherein TEP is conducted based on the specific requirements of the system earmarked for expansion. The transmission expansion planner initiates the process by determining optimal line parameter values that not only meet the operational criteria but also ensure cost-effectiveness. Subsequently, a line is designed to embody these optimal parameters. A detailed comparative analysis is conducted in this study, comparing the outcomes of TEP analyses conducted with conventional lines, unconventional lines, and lines featuring optimal parameters. Through extensive load flow analysis performed under normal and all single-contingency scenarios across three distinct loading conditions (peak load, dominant load representing 60% of peak load, and light load representing 40% of peak load), the results reveal that transmission lines engineered with optimal parameters demonstrate effective operation, with fewer transmission lines required to meet identical demands compared to other approaches. [ABSTRACT FROM AUTHOR]

UAV transmission tower inspection is the use of UAV technology for regular inspection and troubleshooting of towers on transmission lines, which helps to improve the safety and reliability of transmission lines and ensures the stability of the power supply. From the traditional manual tower boarding to the current way of manually selecting target camera shooting points from 3D point clouds to plan the inspection path of the UAV, operational efficiency has drastically improved. However, indoor planning work is still labor-consuming and expensive. In this paper, a deep learning-based point cloud transmission tower segmentation (PCTTS) model combined with the corresponding target point localization algorithm is proposed for automatic segmentation of transmission tower point cloud data and automatically localizing the key inspection component as the target point for UAV inspection. First, we utilize octree sampling with unit ball normalization to simplify the data and ensure translation invariance before putting the data into the model. In the feature extraction stage, we encode the point set information and combine Euclidean distance and cosine similarity features to ensure rotational invariance. On this basis, we adopt multi-scale feature extraction, construct a local coordinate system, and introduce the offset-attention mechanism to enhance model performance further. Then, after the feature propagation module, gradual up-sampling is used to obtain the features of each point to complete the point cloud segmentation. Finally, combining the segmentation results with the target point localization algorithm completes the automatic extraction of UAV inspection target points. The method has been applied to six kinds of transmission tower point cloud data of part segmentation results and three kinds of transmission tower point cloud data of instance segmentation results. The experimental results show that the model achieves mIOU of 94.1% on the self-built part segmentation dataset and 86.9% on the self-built instance segmentation dataset, and the segmentation accuracy outperforms that of the methods for point cloud segmentation, such as PointNet++, DGCNN, Point Transformer, and PointMLP. Meanwhile, the experimental results of UAV inspection target point localization also verify the method's effectiveness in this paper. [ABSTRACT FROM AUTHOR]

Accurate parameters are crucial in modern energy systems to ensure the reliable operation of all components. Given the substantial volume of data in monitored systems, high-performance methods are necessary. This paper proposes a new Bayesian multi-output regressor for estimating the parameters of a three-phase transmission line. The presented approach achieves acceptable accuracy in parameter estimation using only one end of the line. The Bayesian regressor is developed using information derived from the data themselves, eliminating the need to explicitly model the system. This capability allows the method to estimate parameters while accommodating different noise models, even in the presence of systematic errors and non-Gaussian random noise. The methodology was validated on various systems, including a two-bus system, IEEE 14-bus, IEEE 39-bus, and IEEE 118-bus, under diverse conditions such as varying sample sizes, loads, and noise levels. These tests demonstrate the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

The inspection of overhead power transmission lines is of the utmost importance to ensure the power network's uninterrupted, safe, and reliable operation. The increased demand for frequent inspections implementing efficient and cost-effective methods has emerged, since conventional manual inspections are highly inaccurate, time-consuming, and costly and have geographical and weather restrictions. Unmanned Aerial Vehicles are a promising solution for managing automatic inspections of power transmission networks. The project "ALTITUDE (Automatic Aerial Network Inspection using Drones and Machine Learning)" has been developed to automatically inspect the power transmission network of Lesvos Island in Greece. The project combines drones, 5G data transmission, and state-of-the-art machine learning algorithms to replicate the power transmission inspection process using high-resolution UAV data. This paper introduces the ALTITUDE platform, created within the frame of the ALTITUDE project. The platform is a web-based, responsive Geographic Information System (GIS) that allows registered users to upload bespoke drone imagery of medium-voltage structures fed into a deep learning algorithm for detecting defects, which can be either exported as report spreadsheets or viewed on a map. Multiple experiments have been carried out to train artificial intelligence (AI) algorithms to detect faults automatically. [ABSTRACT FROM AUTHOR]

Currently, the intelligent defect detection of massive grid transmission line inspection pictures using AI image recognition technology is an efficient and popular method. Usually, there are two technical routes for the construction of defect detection algorithm models: one is to use a lightweight network, which improves the efficiency, but it can generally only target a few types of defects and may reduce the detection accuracy; the other is to use a complex network model, which improves the accuracy, and can identify multiple types of defects at the same time, but it has a large computational volume and low efficiency. To maintain the model's high detection accuracy as well as its lightweight structure, this paper proposes a lightweight and efficient multi type defect detection method for transmission lines based on DCP-YOLOv8. The method employs deformable convolution (C2f_DCNv3) to enhance the defect feature extraction capability, and designs a re-parameterized cross phase feature fusion structure (RCSP) to optimize and fuse high-level semantic features with low level spatial features, thus improving the capability of the model to recognize defects at different scales while significantly reducing the model parameters; additionally, it combines the dynamic detection head and deformable convolutional v3's detection head (DCNv3-Dyhead) to enhance the feature expression capability and the utilization of contextual information to further improve the detection accuracy. Experimental results show that on a dataset containing 20 real transmission line defects, the method increases the average accuracy (mAP@0.5) to 72.2%, an increase of 4.3%, compared with the lightest baseline YOLOv8n model; the number of model parameters is only 2.8 M, a reduction of 9.15%, and the number of processed frames per second (FPS) reaches 103, which meets the real time detection demand. In the scenario of multi type defect detection, it effectively balances detection accuracy and performance with quantitative generalizability. [ABSTRACT FROM AUTHOR]

This paper describes and validates an advanced synthesis design process of Frequency-Selective Surfaces (FSSs) with elliptic band-stop responses. A systematic procedure based on the Generalized Chebyshev Function and the extracted pole technique enables control of the position of the transmission zeros and the attenuation level to obtain an equiripple rejection response. A systematic process is followed to obtain the lumped LC values of the resonator circuits extracted as poles and the impedance inverters. Then, equivalent dipoles and transmission lines are obtained to carry out the electromagnetic design at normal incidence for a linearly polarized field. The impact of the higher-order modes of the periodic structure on the electrical response of the FSS, which can be relevant due to the stringent selected specifications, has been also analyzed. A fourth-order band-stop filter with a 3 GHz bandwidth centered at 30 GHz and its attenuation at 50 dB has been designed considering three different implementations: two filters using a vacuum as a transmission line with different connection lengths and a third one using a dielectric substrate to enable its manufacturing. In order to verify the design procedure using experimental results, the third filter with printed dipoles in the dielectric substrate has been manufactured and measured, thus validating the developed process. [ABSTRACT FROM AUTHOR]

The paper proposes an ultra-wideband frequency selective rasorber (FSR) with low infrared emissivity for the composite detection threat of both radars and infrared sensors. Firstly, the equivalent circuit (EC) method based on transmission line (TL) theory is utilized to analyze the absorption/transmission conditions. Then, based on the analysis above, sinusoidal microstrip lines with non-frequency-varying characteristics are adopted in the design, which significantly enhances the transmission bandwidth of FSR. The FSR demonstrates an absorption band ranging from 2.65 GHz to 8.80 GHz and a transmission band ranging from 9.15 GHz to 17.71 GHz. Furthermore, an infrared shielding layer (IRSL) exhibiting low emissivity in the infrared band and high transmittance in the microwave band is applied to the FSR. The simulation and experiment results verify that the IRSL-FSR demonstrates an ultra-wide transmission band ranging from 9.16 GHz to 17.94 GHz and an ultra-wide absorption band ranging from 2.66 GHz to 8.01 GHz. Additionally, it exhibits a low emissivity value (0.23) in 8–14 μm, providing a viable solution to the formidable challenge of radar-infrared bistealth for satellites and other communication-enabled flying platforms. [ABSTRACT FROM AUTHOR]

This paper was conceived to investigate some central issues related to the upheaval of current energy scenarios in Sicily. New power connection lines that are about to be built in the Mediterranean area, planned with a view to a constantly increasing renewable generation, encourage the carrying out of analyses on how the Sicilian electric power system will be able to make itself ready to support large power injections, especially due to new renewables plants that will be established in the region soon. This study, carried out in close collaboration with the Italian TSO Terna S.p.A and the University of Palermo, defines what the impacts of new renewable power plants will be on the Sicilian power transmission grid under intact and non-intact grid conditions. This study consists of steady-state simulations carried out using WinCreso® software version 7.62.1-3 in two energy scenarios estimated for the years 2024 and 2027, based on real connection requests by producers to Terna, and allows one to go beyond the studies conducted so far on a 2030 basis through the precise identification of network nodes or lines in difficulty. Finally, as well as presenting an interesting case study due to Sicily's strategic position in the Mediterranean Sea, this article proposes a methodological approach that can easily be adopted in other contexts and by other TSOs to analyze similar situations. [ABSTRACT FROM AUTHOR]

The effective exploitation of renewable energy sources is one of the most effective solutions to counter the energy, environmental and economic problems associated with the use of fossil fuels. Small-scale wind turbines (converting wind energy into electric energy with a power output lower than 50 kW) have received tremendous attention over the past few decades thanks to their reduced environmental impact, high efficiency, low maintenance cost, high reliability, wide wind operation range, self-starting capability at low wind speed, limited installation space, reduced dependence on grid-connected power and long transmission lines, low capital costs, as well as the possibility to be installed in some urban settings. However, there are significant challenges and drawbacks associated with this technology from many different perspectives, including the significant discrepancy between theoretical performance data provided by the manufacturers and real field operation, that need to be investigated in greater depth in order to enable a more widespread deployment of small-scale wind turbines. In this review, a complete and updated list of more than 200 commercially available small-scale horizontal and vertical wind turbine models is provided and analysed, detailing the corresponding characteristics in terms of the number and material of blades, start-up wind speed, cut-in wind speed, cut-out wind speed, survival wind speed, maximum power, noise level, rotor diameter, turbine length, tower height, and specific capital cost. In addition, several scientific papers focusing on the experimental assessment of field performance of commercially available small-scale horizontal and vertical wind turbines have been reviewed and the corresponding measured data have been compared with the rated performance derived from the manufacturers' datasheets in order to underline the discrepancies. This review represents an opportunity for the scientific community to have a clear and up-to-date picture of small-scale horizontal as well as vertical wind turbines on the market today, with a precise summary of their geometric, performance, and economic characteristics, which can enable a more accurate and informed choice of the wind turbine to be used depending on the application. It also describes the differences between theoretical and in-situ performance, emphasizing the need for further experimental research and highlighting the direction in which future studies should be directed for more efficient design and use of building-integrated small-scale wind turbines. [ABSTRACT FROM AUTHOR]

Ice-covered transmission lines seriously affect the normal operation of the power transmission system. Resonance deicing based on different ice thicknesses is an effective method to solve the issue of ice-covered transmission lines. In order to obtain accurate ice thickness of transmission lines, this paper designs an ice thickness of transmission line recognition model based on Global Micro Strip Awareness Net (GMSA-Net) and proposes a Mixed Strip Convolution Module (MSCM) and a global micro awareness module (GMAM). The MSCM adapts to the shape of ice-covered transmission lines by using strip convolutions with different receptive fields, improving the encoder's ability to extract ice-covered features; the GMAM perceives through both global and micro parts, mining the connections between semantic information. Finally, the ice thickness of the generated segmented image is calculated using the method of regional pixel statistics. Experiments are conducted on the dataset of ice-covered transmission lines. The mean Intersection over Union (mIoU) of image segmentation reaches 96.4%, the balanced F-Score (F1-Score) is 98.1%, and the identification error of ice thickness is within 3.8%. Experimental results prove that this method can accurately identify the ice thickness of transmission lines, providing a control basis for the application of resonant deicing engineering. [ABSTRACT FROM AUTHOR]

Electric pylons are crucial components of power infrastructure, requiring accurate detection and identification for effective monitoring of transmission lines. This paper proposes an innovative model, the EP-YOLOv8 network, which incorporates new modules: the DSLSK-SPPF and EMS-Head. The DSLSK-SPPF module is designed to capture the surrounding features of electric pylons more effectively, enhancing the model's adaptability to the complex shapes of these structures. The EMS-Head module enhances the model's ability to capture fine details of electric pylons while maintaining a lightweight design. The EP-YOLOv8 network optimizes traditional YOLOv8n parameters, demonstrating a significant improvement in electric pylon detection accuracy with an average mAP@0.5 value of 95.5%. The effective detection of electric pylons by the EP-YOLOv8 demonstrates its ability to overcome the inefficiencies inherent in existing optical satellite image-based models, particularly those related to the unique characteristics of electric pylons. This improvement will significantly aid in monitoring the operational status and layout of power infrastructure, providing crucial insights for infrastructure management and maintenance. [ABSTRACT FROM AUTHOR]

Transmission lines are affected by external environmental factors such as strong winds and ice cover. In recent years, extreme weather events have increased, leading to recurrent disturbances in transmission lines because of wind deflection. These incidents have resulted in significant financial losses and have disrupted regular industrial and domestic activities. In this paper, the ANSYS Workbench 2020 R2 finite element analysis platform was used to establish a transmission line-hanging insulator string system model. Calculations on transmission lines were conducted considering variations in different stall spacing, height differences, wind speed, and the wind attack angle. The impact of these diverse factors on the wind deflection of insulators was scrutinized, leading to the derivation of patterns describing how the wind deflection angle shifts in response to changes in stall spacing, height differences, wind speed, and the wind attack angle. Based on the generalized linear regression network and particle swarm improved support vector machine algorithm, a meteorological prediction-based early warning method for wind deflection of transmission lines was proposed, a transmission line wind deflection early warning model was established, and the practical effect of the model was evaluated. The outcomes of this study provide crucial data for the formulation and development of ultra-high voltage (UHV) and extra-high voltage (EHV) transmission networks. Furthermore, they can contribute to the advanced detection of wind deflection issues. [ABSTRACT FROM AUTHOR]

This paper analyzes the behavior of an electrical power system when N-1 contingencies occur in the transmission stage, which can be produced by incorrect operation of the protection relays, phenomena of natural origin, or increased loadability, which affect the operation and reliability of the electrical system. The operation output of a transmission line results in the variation of the nominal values of the electrical parameters involved because they disturb the stability of the generation, transmission systems, and the supply of electrical energy to the loads, such as voltages and angles of the nodes and the active and reactive power of the system. The proposed methodology was based on analyzing the different electrical parameters of the power system, quantifying the contingency index in a state of regular operation, and comparing it to operation in contingency N-1, with which the most severe contingency was determined and, therefore, achieved; identifying contingencies that can cause system collapses; improving the contingency index from 23.08555 to 22.9276624 for the L16–19 contingency and to 22.9795235 for the L21–22 contingency, which are the most severe contingencies determined with the proposed methodology. To test the proposed methodology, the IEEE 39 bus-bar test system was considered, and the elements that should be implemented to avoid the vulnerability of the power system to N-1 contingencies were determined. [ABSTRACT FROM AUTHOR]

With the increasing demand for electricity, the power grid is undergoing significant advancements. Insulators, which serve as protective devices for transmission lines in outdoor high-altitude power systems, are widely employed. However, the detection of defects in insulators captured under challenging conditions, such as rain, snow, fog, sunlight, and fast-moving drones during long-distance photography, remains a major challenge. To address this issue and improve the accuracy of defect detection, this paper presents a novel approach: the Multi-Scale Insulator Defect Detection Approach using Detection Transformer (DETR). In this study, we propose a multi-scale backbone network that effectively captures the features of small objects, enhancing the detection performance. Additionally, we introduce a self-attention upsampling (SAU) module to replace the conventional attention module, enhancing contextual information extraction and facilitating the detection of small objects. Furthermore, we introduce the insulator defect (IDIoU) loss, which mitigates the instability in the matching process caused by small defects. Extensive experiments were conducted on an insulator defect dataset to evaluate the performance of our proposed method. The results demonstrate that our approach achieves outstanding performance, particularly in detecting small defects. Compared to existing methods, our approach exhibits a remarkable 7.47% increase in the average precision, emphasizing its efficacy in insulator defect detection. The proposed method not only enhances the accuracy of defect detection, which is crucial for maintaining the reliability and safety of power transmission systems but also has broader implications for the maintenance and inspection of high-voltage power infrastructure. [ABSTRACT FROM AUTHOR]

Power transmission lines in Ethiopia are experiencing an alarmingly high frequency of unexplained outages triggered by environmental factors, which significantly undermine the reliability of the country's power system. This paper presents investigations aiming to identify those among the unexplained fault records that have been caused by pollution induced flashovers. An identification method is developed, which associates the contextual fault features, such as information about the characteristics of the fault, fault location, and time of day, as well as month of its occurrence with local meteorological/climatic and environmental conditions. A total number of 4231 unexplained faults, recorded between 2015 and 2022, were analyzed. Among them, 1045 faults (24.7%) were identified as being most likely caused by pollution induced flashovers. The entire network suffered from more than 130 pollution-induced flashovers annually with a frequency of about 0.8 faults/100 km/year. The fault frequency strongly differed among the grid regions, being highest in the Northeast and lowest in the Southwest region. Moreover, the performed analyses also concentrated on the evaluation of the pollution performance of various insulator types employed in the network. The results indicate that porcelain insulators have the highest pollution-induced flashover intensity of 1.47 faults/year/1000 units, followed by silicone rubber polymeric composite insulators and glass insulators with the respective intensities of 1.21 and 0.83 faults/year/1000 units. These results indicate that despite the high expectations towards the pollution performance of silicone rubber polymeric insulators, their use in the Ethiopian climatic and environmental conditions appears to be unsatisfactory. [ABSTRACT FROM AUTHOR]

High Voltage Direct Current power links are usually designed to adopt, on a continuous basis or during emergency operations, two grounding plants using the soil or seawater as a link–current return path. Such DCs flowing into the ground can cause problems, of which one of the most important is the increased risk of the corrosion of metallic structures in the area. Normally, the simplest mitigation technique is to keep an adequate distance between the main grounding plants and any metallic structure that can be corroded. Normally, such distances are not less than 5 km. However, there are situations where this approach cannot be applied, for example due to geographical constraints. In this paper, we describe and analyze the behavior of a mitigation technique that can be adopted when the HV pole cable is laid closer than the recommended distance to the main ground electrode. This paper is focused on the minimization of deleterious effects on the cable's metallic sheath and its earthing points, distributed along it by means of sheath segmentation. The suggested approach appears well-suited to substantially diminishing the current flowing through the sheath of an HVDC power cable. In the segmented scenario, the sheath's power dissipation is less than one-hundredth of that in the typical continuous configuration. [ABSTRACT FROM AUTHOR]

To address the fuzzy reconstruction effect on distant objects in unbounded scenes and the difficulty in feature matching caused by the thin structure of power lines in images, this paper proposes a novel image-based method for the reconstruction of power transmission lines (PTLs). The dataset used in this paper comprises PTL progressive motion sequence datasets, constructed by a visual acquisition system carried by a developed Flying–walking Power Line Inspection Robot (FPLIR). This system captures close-distance and continuous images of power lines. The study introduces PL-NeRF, that is, an enhanced method based on the Neural Radiance Fields (NeRF) method for reconstructing PTLs. The highlights of PL-NeRF include (1) compressing the unbounded scene of PTLs by exploiting the spatial compression of normal L ∞ ; (2) encoding the direction and position of the sample points through Integrated Position Encoding (IPE) and Hash Encoding (HE), respectively. Compared to existing methods, the proposed method demonstrates good performance in 3D reconstruction, with fidelity indicators of PSNR = 29, SSIM = 0.871, and LPIPS = 0.087. Experimental results highlight that the combination of PL-NeRF with progressive motion sequence images ensures the integrity and continuity of PTLs, improving the efficiency and accuracy of image-based reconstructions. In the future, this method could be widely applied for efficient and accurate 3D reconstruction and inspection of PTLs, providing a strong foundation for automated monitoring of transmission corridors and digital power engineering. [ABSTRACT FROM AUTHOR]

This document is a correction notice for a research paper titled "A Drone Based Transmission Line Components Inspection System with Deep Learning Technique." The authors have identified errors in the figures and main body paragraphs of the paper and have provided corrected versions of the figures. They have also made changes to the abstract and introduction sections. The paper discusses the use of a drone to detect and analyze transmission line components for defects, utilizing deep learning techniques and convolutional neural networks. The authors acknowledge the errors in the original paper and apologize for any confusion caused. The research is funded by the Korean Ministry of the Interior and Safety. The authors of the paper are Zahid Ali Siddiqui and Unsang Park. [Extracted from the article]

This paper presents an extensive review of the most effective and modern monitoring methods for electrical power lines, with particular attention to high-voltage (HV) and medium-voltage (MV) systems. From a general point of view, the main objective of these techniques is to prevent catastrophic failures by detecting the partial damage or deterioration of components and allowing maintenance operations to be organized. In fact, the protection devices commonly used in transmission and distribution networks guarantee the location of faults, such as short-circuits, putting the non-functioning branch of the network out of service. Nowadays, alongside these devices, it is possible to introduce new intelligent algorithms capable of avoiding the total loss of functionality, thus improving the reliability of the entire network. This is one of the main challenges in modern smart grids, which are characterized by the massive integration of renewable energy sources and a high level of complexity. Therefore, in the first part of this paper, a general overview of the most common protection devices is proposed, followed by an analysis of the most modern prevention algorithms. In the first case, the coordination of the relays plays a fundamental role in obtaining the fault location with a high level of selectivity, while in the field of preventive analysis, it is necessary to address the implementation of artificial intelligence methods. The techniques presented in this paper provide a comprehensive description of the different monitoring approaches currently used in distribution and transmission lines, highlighting the coordination of protection relays, the computational algorithms capable of preventing failures, and the influence of the distributed generation in their management. Therefore, this paper offers an overview of the main diagnostic techniques and protection devices, highlights the critical issues that can be overcome through the introduction of artificial intelligence, and describes the main prognostic methods, focusing on their invasive level and the possibility of operating directly online. This work also highlights the main guidelines for the classification and choice between the different approaches. [ABSTRACT FROM AUTHOR]

Renewable energy sources have a multifaceted impact on power grids, ranging from the reliability and quality of electricity to the selective impact on equipment. While renewables used to be distributed in distribution networks, now their capacity is commensurate with thermal power plants and their impact on the grid should not be underestimated. According to the statistics on the interruption of the bulk electric networks, one of the main reasons for emergency shutdowns of extra high-voltage power lines are single-phase short circuits. The problem of mathematical modeling of the limit modes in terms of static stability is very relevant to the design and operation of electric power systems (EPS). Calculations of limit modes have both an independent value and a component of other electrical engineering tasks related to ensuring the required level of reliability and cost-effectiveness of the operation of the united PS. Despite the great degree of development of issues of planning and control of electric modes, system accidents associated with unacceptable loads of network elements occur in the Ukrainian energy industry. Non-phase modes regularly occur in electric power systems, which can lead to an unacceptable load of intersystem network elements, which imposes significant restrictions on their throughput. Single-phase short circuits are more than 95% of other damage that occurs in the line. The use of single-phase auto reclose on the transmission lines allows disconnecting only the damaged phase for a short period of time and not the entire transmission line. This action preserves the transit of electricity along the line and prevents the violation of the stability of parallel operation. To achieve this, the current-free pause of the single-phase auto reclose should last as short as possible. On the other hand, an important task to be solved when using single-phase auto reclose is to choose the minimum duration of the current-free pause necessary for its success. The problem studied in this paper deals with the safety and correct operation of transmission lines (TS) of the Ukrainian bulk power system in special conditions (not predictable, changing due to frequent attacks). For a quickly changing configuration, the power grid uses switches, and in the case of ultra-high voltage, the TS needs to solve the problem of secondary arc currents and recovering stresses in the place of arc burning after its extinction. One of the methods of reducing secondary arc currents and recovering stresses in the place of arc burning after its extinction is the implementation of single-phase automatic reclosing (SPAR). The main theoretical result of the paper is a proposed mathematical model of a compensated power transmission line based on the use of matrix n-poles, which makes it possible to model in detail stationary power transmission modes, including the SPAR mode. The proposed mathematical model of three-phase power transmission has been created using phase coordinates and can be used for the analysis of complex asymmetric modes. The main practical result of the paper is physically interpreted simplified models of three-phase TS, which can be used for the study of resonant overvoltages and currents of the feeding arc in non-full-phase circuit SPAR. The conclusion that can be drawn from the obtained results points out which line lengths must take into account the influence of longitudinal asymmetry when choosing the inductive resistance, i.e., take into account the dependence of the mode parameters on the location of the damaged phase. The observed results show that the largest values of the multiplicity of overvoltages will take place in phase B. The novelty of the work is the developed technique that makes it possible to determine in advance, depending on the disconnected phase of the line, the values of the primary conductivities of the STC (static thyristor compensators) and the corresponding angles of control of the thyristor switches, which satisfy almost complete compensation of the secondary arc at any point of the line in the specific condition (hard) of the bulk power system operation. [ABSTRACT FROM AUTHOR]

Insulators are an important part of transmission lines in active distribution networks, and their performance has an impact on the power system's normal operation, security, and dependability. Traditional insulator detection methods, on the other hand, necessitate a significant amount of labor and material resources, necessitating the development of a new detection method to substitute manpower. This paper investigates the abnormal condition detection of insulators based on UAV vision sensors using artificial intelligence algorithms from small samples. Firstly, artificial intelligence for the image data volume requirements was large, i.e., the insulator image samples taken by the UAV vision sensor inspection were not enough, or there was a missing image problem, so the data enhancement method was used to expand the small sample data. Then, the YOLOV5 algorithm was used to compare detection results before and after the extended dataset's optimization to demonstrate the expanded dataset's dependability and universality, and the results revealed that the expanded dataset improved detection accuracy and precision. The insulator abnormal condition detection method based on small sample image data acquired by the visual sensors studied in this paper has certain theoretical guiding significance and engineering application prospects for the safe operation of active distribution networks. [ABSTRACT FROM AUTHOR]

The accurate voltage measurement of distribution networks is of great significance in power dispatching and fault diagnosis. Voltage sensors based on the spatial electric field effect do not require grounding, which provides the possibility for the distributed measurement of transmission line voltages. However, the divider ratio of suspension grounding voltage sensors is affected by the height between the sensor and the ground, as well as the distance between the sensor and the telegraph pole. In this paper, a self-calibration method based on internal capacitance transformation is proposed to realize the on-line calibration of suspension grounding voltage sensors. The calibration is accomplished by switching different parameters in the conditioning circuit, and the calibration process does not require power failure or known input excitation. In addition, the impact of electric fields in the other two phases of three-phase transmission lines on measurement through simulation research is quantified in this paper. In order to reduce the impact of interference electric fields, an equipotential shielding structure is designed. The circuit topology and probe prototype have been developed and testing has been conducted in laboratory conditions; the experimental results show that the maximum relative error of voltage amplitude is 1.65%, and the phase relative error is 0.94%. The measurement accuracy is not limited by the height to ground or the distance to the telegraph pole. In addition, in the application of an equipotential shielding probe, the maximum deviation of measured voltage is 0.7% with and without interference electric fields. [ABSTRACT FROM AUTHOR]

This paper investigates the manufacturing uncertainties at a 60 GHz millimeter-wave band for the monolithic hybrid microwave integrated circuits (MHMIC) fabrication process. It specifically deals with the implementation tolerances of thin-film gold microstrip transmission lines, titanium oxide thin-layer resistors, microstrip quarter-wavelength radial stubs, and active device implementation using the gold-bonding ribbons. The impacts of these manufacturing tolerances are assessed and experimentally quantified through prototyped MHMIC circuits. This allows us, on one hand, to identify the acceptable amount of dimensional variation enabling reasonable performances. On the other hand, it aims to establish a relationship between the manufacturing tolerances and the circuit parameters to provide more flexibility for the tolerance compensation and accuracy enhancement of the MHMIC fabrication processes. [ABSTRACT FROM AUTHOR]

The interline power flow controller (IPFC) is one of the most versatile integrated flexible alternating current transmission systems (FACTS) controllers and can realize power flow control for multiple transmission lines in modern power systems. However, control characteristics are ignored in conventional IPFC models, in which unreasonable assumptions about injected voltages may lead to security problems in realistic operation. Besides, preventive security constraints considering IPFC control modes are not included in optimal power flow (OPF) control of the system with IPFC, squandering IPFC control potential. To solve these problems, a preventive-security-constrained optimal power flow (PSCOPF) model considering IPFC control modes is proposed in this paper. IPFC control characteristics under different control modes are analyzed and employed as constraints of the optimization model. The iterative updates of converter output voltages for different control modes are derived respectively for power flow calculation, and the power and voltages required in the objective function and constraints of the proposed model can then be obtained. Through optimal selection of IPFC control modes and control parameters, the proposed model can better reconcile the economical and secure operation of the system. Numerical results demonstrate the efficient performance and superiority of the PSCOPF model considering IPFC control modes. [ABSTRACT FROM AUTHOR]

In order to evaluate the risk of transmission line tripping due to wildfires, it is necessary to predict the breakdown voltage of the insulation gap under the flame. Firstly, this paper studies the breakdown prediction of rod–plane gaps under the full-flame bridging of wooden cribs; it then obtains the breakdown voltage and the leakage current values of full-flame bridging considering different sizes of wooden cribs and different gap distances. Then, a multi-physical field simulation is carried out to obtain the flame gap characteristic parameters, such as spatial temperature. The feature quantity is normalized and reduced in dimension, and a prediction model for gap breakdown voltage under flame conditions based on a support vector machine (SVM) is established. Finally, the DC withstand voltage values and corresponding characteristic quantities under different flame gap conditions are used as sample sets to test the prediction model. The results show that the prediction error for small gap breakdown voltage is less than 2.6%. The samples were tested under different flame intensities for training and prediction, and the error was less than 3.3%. The small gap data for 30~60 cm is used to predict the breakdown voltage of the long gap for 100~140 cm, and the error is less than 3.2%. Compared with the fitting correction formula method proposed in existing research, the error is reduced by 11.5% and 4.4%, respectively, which verifies the effectiveness of the SVM prediction model. [ABSTRACT FROM AUTHOR]

Ensuring the safety of transmission lines necessitates effective insulator defect detection. Traditional methods often need more efficiency and accuracy, particularly for tiny defects. This paper proposes an innovative insulator defect recognition method leveraging YOLOv8s-SwinT. Combining Swin Transformer and Convolutional Neural Network (CNN) enhances the model's understanding of multi-scale global semantic information through cross-layer interactions. The improved BiFPN structure in the neck achieves bidirectional cross-scale connections and weighted feature fusion during feature extraction. Additionally, a new small-target detection layer enhances the capability to detect tiny defects. The experimental results showcase outstanding performance, with precision, recall, and mAP reaching 95.6%, 95.3%, and 97.7%, respectively. This boosts detection efficiency and ensures high accuracy, providing robust support for real-time detection of tiny insulator defects. [ABSTRACT FROM AUTHOR]

AC-DC hybrid distribution grids realize power transmission through tie lines. Accurately characterizing the power exchange capacity between regional grids while ensuring safe grid operation is the basis for the coordinated scheduling of resources in interconnected distribution grids. However, most of the current AC/DC hybrid models are linear, and it is challenging to ensure the accuracy criteria of the obtained feasible regions. In this paper, a two-stage multi-segment boundary approximation method is proposed to characterize the feasible region of hybrid distribution grid tie line operation. Information such as security operation constraints are mapped to the feasible region of the boundary tie line to accurately characterize the transmission exchange capacity of the tie line. To avoid the limitations of linear models, the method uses a nonlinear model to iteratively search for boundary points of the feasible region. This ensures high accuracy in approximating the real feasible region shape and capacity limitations. A convolutional neural network (CNN) is then utilized to map the given boundary and cost information to obtain an estimated equivalent operating cost function for the contact line, overcoming the inability of previous methods to capture nonlinear cost relationships. This provides the necessary cost information in a data-driven manner for the economic dispatch of hybrid AC-DC distribution networks. Numerical tests demonstrate the effectiveness of the method in improving coordination accuracy while preserving regional grid privacy. The key innovations are nonlinear modeling of the feasible domain of the contact line and nonlinear cost fitting for high-accuracy dispatch. [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]

Modular multilevel converters (MMCs) are susceptible to subsynchronous oscillations (SSOs) caused by impedance interactions in the power line. Current research into the stability of MMCs focuses mainly on voltage feed-forward control, while the effect of current feed-forward control is neglected. This paper proposes a current feed-forward compensation method based on impedance reshaping for standalone MMCs. Initially, an impedance model was developed to identify the stability risks caused by the interaction between the MMC and power line impedance. The proposed method feeds the current compensation signal into the modulation circuit, thereby improving the control signal and suppressing the impedance interaction between the MMC and the power line. The analysis of the harmonic state space (HSS) method verifies that the proposed approach effectively reduces the negative damping region in the frequency band where the SSO is located. Additionally, the impedance frequency scanning method confirms the accuracy of impedance modeling. Using the MATLAB/Simulink platform and StarSim HIL hardware-in-the-loop experimental platform, the SSO phenomenon of the MMC is simulated, and the results show that the proposed method can effectively suppress harmonic currents during SSO, which verifies the accuracy of the stability analysis and the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This paper aims to design the fundamental basis for an Unmanned Aerial System (UAS)-driven, remote, and non-invasive current sensing application. Using the COMSOL software, the methodology presented here consists of the Computer Aided Design (CAD) for stranded Transmission Line (TL) geometries composed of 7 to 91 sub-filaments and discretized via tetrahedral-element-based meshes. The radiated Magnetic Field (MF) around each TL is then solved by means of Finite Element Analysis (FEA) after selecting the proper materials for TLs under the coil geometry analysis study. For each TL, all resultant MFs' norms are presented as tabulated data, with respect to the inducing currents. Eventually, the complex mathematical model needed to evaluate these MFs, radiated around stranded TLs, is surpassed by the scalable models designed through this study. The min/max MFs radiated around each TL resulting from the min/max injected current values are hence obtained. This would serve in the accurate choosing/positioning of magnetic-based sensors in UAS applications, reliably. Additionally, related future works are concretely presented. [ABSTRACT FROM AUTHOR]

A meta-surface-based arbitrary bandwidth filter realization method for terahertz (THz) future communications is presented. The approach involves integrating a meta-surface-based bandstop filter into an ultra-wideband (UWB) bandpass filter and adjusting the operating frequency range of the meta-surface bandstop filter to realize the design of arbitrary bandwidth filters. It effectively addresses the complexity of designing traditional arbitrary bandwidth filters and the challenges in achieving impedance matching. To underscore its practicality, the paper employs silicon substrate integrated gap waveguide (SSIGW) and this method to craft a THz filter. To begin, design equations for electromagnetic band gap (EBG) structures were developed in accordance with the requirements of through-silicon via (TSV) and applied to the design of the SSIGW. Subsequently, this article employs equivalent transmission line models and equivalent circuits to conduct theoretical analyses for both the UWB passband and the meta-surface stopband portions. The proposed THz filter boasts a center frequency of 0.151 THz, a relative bandwidth of 6.9%, insertion loss below 0.68 dB, and stopbands exceeding 20 GHz in both upper and lower ranges. The in-band group delay is 0.119 ± 0.048 ns. Compared to reported THz filters, the SSIGW filter boasts advantages such as low loss and minimal delay, making it even more suitable for future wireless communication. [ABSTRACT FROM AUTHOR]

This paper introduces a fault location technique for multi-section combined transmission lines, utilizing unsynchronized fault record data. The approach determines the intersection points of the voltage magnitudes computed using the fault current and voltage records of each terminal and the constant line parameters of each section. Finally, the real fault location is identified based on the calculated inverse angle of equivalent impedance. To evaluate the performance of the proposed algorithm, a three-section combined transmission line is constructed using the PSCAD/EMTDC software version 5.0.0, and various fault scenarios are simulated and tested employing the proposed algorithm. The results indicate that the proposed algorithm is applicable to various types of faults in a multi-section combined transmission line; the estimations are accurate. [ABSTRACT FROM AUTHOR]

The aging process of transmission lines has a direct impact on the reliability and safety of the power grid. Therefore, an accurate age estimation method is imperative for effective maintenance planning and infrastructure investment. This paper introduces a systematic methodology for estimating the age of overhead transmission lines, utilizing the percentage statistical health index (%SHI) and the failure probability curve-fitting (FPCF) method. The %SHI, employing a scoring and weighting approach derived from test results and inspections, is used to assess the actual condition of transmission line equipment. Additionally, the FPCF approach is applied to illustrate the connection between the SHI and the likelihood of failure, facilitating the assessment of transmission line age by fitting failure probability curves to the SHI data. This age is directly associated with the probability of experiencing a failure. The evaluation was conducted on 924 towers situated along four transmission lines connecting the 115 kV substations S1–S2, S3–S4, S5–S6, and S7–S8. These transmission lines are in four regions with diverse terrain and environments such as mountains, rice fields, and more. In the SHI calculation, practical testing results and historical failure data were applied. The results clearly indicate that there were notable disparities in the age estimations for transmission lines in diverse geographical regions of Thailand when compared to their actual ages. These discrepancies can be attributed to various factors, including the local environment, such as rainfall, flooding, and salt-laden air as well as specific geographical features like mountainous and coastal terrain. To mitigate the deterioration of transmission lines in all regions, it is essential to implement a proactive maintenance strategy. This strategy should involve more frequent inspections, the use of advanced monitoring technologies, and the establishment of robust maintenance procedures, with which it would become possible to enhance the accuracy of equipment condition assessments, ultimately resulting in an overall improvement in the reliability of transmission lines. [ABSTRACT FROM AUTHOR]

Insulator defect detection of transmission line insulators is an important task for unmanned aerial vehicle (UAV) inspection, which is of immense importance in ensuring the stable operation of transmission lines. Transmission line insulators exist in complex weather scenarios, with small and inconsistent shapes. These insulators under various weather conditions could result in low-quality images captured, limited data numbers, and imbalanced sample problems. Traditional detection methods often struggle to accurately identify defect information, resulting in missed or false detections in real-world scenarios. In this paper, we propose a weather domain synthesis network for extracting cross-modality discriminative information on multi-domain insulator defect detection and classification tasks. Firstly, we design a novel weather domain synthesis (WDSt) module to convert various weather-conditioned insulator images to the uniform weather domain to decrease the existing domain gap. To further improve the detection performance, we leverage the attention mechanism to construct the Cross-modality Information Attention YOLO (CIA-YOLO) model to improve the detection capability for insulator defects. Here, we fuse both shallow and deep feature maps by adding the extra object detection layer, increasing the accuracy for detecting small targets. The experimental results prove the proposed Cross-modality Information Attention YOLO with the weather domain synthesis algorithm can achieve superior performance in multi-domain insulator datasets (MD-Insulator). Moreover, the proposed algorithm also gives a new perspective for decreasing the multi-domain insulator modality gap with weather-domain transfer, which can inspire more researchers to focus on the field. [ABSTRACT FROM AUTHOR]

At this stage, the inspection of transmission lines is dominated by UAV inspection. Insulators, as essential equipment for transmission line equipment, are susceptible to various factors during UAV detection, and their detection results often lead to leakages and false detection. Combining deep learning detection algorithms with the UAV transmission line inspection system can effectively solve the current sensing problem. To improve the recognition accuracy of insulator detection, the MS-COCO pre-training strategy that combines the FPN module with a cascading R-CNN algorithm based on the ResNeXt-101 network is proposed. The purpose of this paper is to systematically and comprehensively analyze mainstream isolator detection algorithms at the current stage and to verify the effectiveness of the improved Cascade R-CNN X101 model by combining the mAP (mean Average Precision) value and other related evaluation indices. Compared with Faster R-CNN, Retina Net, and other detection algorithms, the model is highly accurate and can effectively deal with the false detection, leakage, and non-recognition of the environment in online special detection. The research in this paper provides a new idea for intelligent fault detection of transmission line insulators and has some reference value for engineering applications. [ABSTRACT FROM AUTHOR]

Conducted emissions (CE) for three-phase systems are becoming an increasing concern due to the recent exponential growth of three-phase applications, especially linked to the automotive sector. The problem arises because electromagnetic compatibility (EMC) standards only define the methodology to measure the CE generated by the equipment under test (EUT), and they do not provide sufficient information to design a power line filter (PLF) in case of non-compliance. Hence, the design of an optimal PLF is a very difficult task for engineers. The unclear methodology to be followed, unknown load impedances, inadequate equipment, and lack of knowledge of the modal noise are all different reasons that contribute to increasing the PLF design complexity. Common mode (CM) and differential mode (DM) decomposition and PLF design techniques for single-phase EUTs are well discussed and studied in the literature, but the same cannot be stated when it comes to three-phase PLF design. The objective of this paper is to clarify how modal noises behave in a three-phase system and to propose a clear methodology which can be followed to design an optimal three-phase PLF. Additionally, this paper analyses and discusses the modal noises' intrinsic behavior and provides an understanding of how the PLF components behave when subjected to either a CM or DM noise. Finally, a methodology to design a three-phase PLF, based on accurate insertion loss (IL) estimations and S-parameter measurements, is used to determine the optimal PLF. This approach is tested and validated. [ABSTRACT FROM AUTHOR]