Search Results (138)

Icing disasters can significantly reduce the reliability of overhead transmission lines, while limited budgets of power grid enterprises constrain the scale of investment. To improve investment efficiency, it is essential to balance the reliability and economic performance of anti-icing strategies. Most existing studies on the performance evaluation of anti-icing strategies for transmission lines focus primarily on reliability, neglecting their economic implications. To address this gap, this paper proposes a time-evolution-based performance evaluation model for overhead transmission line anti-icing strategies. First, a lifetime distribution function of transmission lines during the icing period is constructed based on the Nelson–Aalen method and metal deformation theory. Subsequently, a quantitative risk model for iced transmission lines is developed, incorporating the failure rate, value of lost load, and amount of lost load, providing a monetary-based indicator for icing risk. Finally, a performance evaluation method for anti-icing strategies is developed based on the risk quantification model. Implementation cost is treated as risk control expenditure, and strategy performance is assessed by integrating it with residual risk cost to identify the optimal strategy through composite cost analysis. The proposed model enables a comprehensive assessment of anti-icing strategy performance, improving the accuracy of strategy selection and achieving a dynamic balance between implementation cost and transmission line reliability. The case study results demonstrate that the proposed method effectively reduces the risk of failure in overhead transmission lines under ice disasters while lowering anti-icing costs. Compared with two existing strategy selection approaches, the strategy based on this method achieved 46.11% and 32.56% lower composite cost, and 60.26% and 48.41% lower residual risk cost, respectively. Full article

Power transmission infrastructure requires continuous inspection to prevent failures and ensure grid stability. UAV-based systems, enhanced with deep learning, have emerged as an efficient alternative to traditional, labor-intensive inspection methods. However, most existing approaches rely on separate models for cable segmentation and anomaly detection, leading to increased computational overhead and reduced reliability in real-time applications. To address these limitations, we propose PowerLine-MTYOLO, a lightweight, one-stage, multitask model designed for simultaneous power cable segmentation and broken strand detection from UAV imagery. Built upon the A-YOLOM architecture, and leveraging the YOLOv8 foundation, our model introduces four novel specialized modules—SDPM, HAD, EFR, and the Shape-Aware Wise IoU loss—that improve geometric understanding, structural consistency, and bounding-box precision. We also present the Merged Public Power Cable Dataset (MPCD), a diverse, open-source dataset tailored for multitask training and evaluation. The experimental results show that our model achieves up to +10.68% mAP@50 and +1.7% IoU compared to A-YOLOM, while also outperforming recent YOLO-based detectors in both accuracy and efficiency. These gains are achieved with a smaller model memory footprint and a similar inference speed compared to A-YOLOM. By unifying detection and segmentation into a single framework, PowerLine-MTYOLO offers a promising solution for autonomous aerial inspection and lays the groundwork for future advances in fine-structure monitoring tasks. Full article

Inductive power transfer (IPT) systems are transforming railway infrastructure by enabling efficient, wireless energy transmission for electric locomotives equipped with Li-ion batteries. This technology eliminates the need for overhead power lines and third rails, offering financial and operational advantages over conventional electric propulsion systems. Despite its potential, IPT deployment in rail applications faces significant challenges, including the fragility of materials (i.e., ferrite and Litz wires), thermal management during high-power transfers, and electromagnetic interference (EMI) on the transmitter side. This study discusses several factors affecting IPT efficiency and introduces magnetic concrete as a durable and cost-effective material solution for IPT systems. Magnetic concrete combines soft ferrite powder with water and coarse aggregates to enhance magnetic functionality while maintaining structural strength comparable to conventional concrete. Its durability and optimized magnetic properties promote consistent power transfer efficiency, making it a viable alternative to traditional ferrite cores. A comparative study has been performed on non-magnetic and magnetic concrete (with 33% ferrite powder) using both permeability tests and finite element analysis (FEA). The FEA includes both thermal and electromagnetic simulations using Ansys Maxwell (v.16), revealing that magnetic concrete can improve temperature management and EMI mitigation, and the findings underscore its potential to revolutionize IPT technology by overcoming the limitations of traditional materials and enhancing durability, cost-efficiency, and power transfer efficiency. By addressing the challenges of fragility, thermal management, and shielding of the unique coil topology design presented, this study lays the groundwork for improving IPT infrastructure in sustainable and efficient rail transport systems. Full article

Most transmission system operators (TSOs) use seasonally static models considering extreme weather conditions, serving as a reference for computing the transmission capacity of power lines. The use of dynamic line rating (DLR) models can avoid the construction of new lines, market splitting, false congestions and the degradation of lines in a cost-effective way. The operation of power systems is planned based on market results, which consider transactions hours ahead of real-time operation using forecasts with errors. The same is true for the DLR. So, during real-time operation TSOs should rapidly compute the DLR of overhead lines to avoid considering an ampacity above their lines’ design, reflecting the real-time weather conditions. Considering that the DLR of the lines can affect the power flow of an entire region, the use of the complete indirect DLR methodology has a high computation burden for all sectors and lines in a region. So, this article presents and tests three pre-solve methodologies able to rapidly identify the critical sector of each line. These methodologies solve the problem of the high computation burden of the CIGRÉ thermodynamic model of overhead lines. They have been tested by using real data of the transmission grid and the weather conditions for two different regions in Portugal, leading to errors in the computation of the DLR lower than 1% in relation to the complete CIGRÉ model, identifying the critical sector in significantly less time. Full article

Lightning strikes are a leading cause of outages on overhead transmission lines, significantly compromising power system reliability. Consequently, monitoring lightning activity is critical to mitigate its impact on lines with high outage rates. This study presents an autonomous lightning strike counter system utilizing a split-core Rogowski coil for non-invasive current measurement on transmission towers. The system combines the Rogowski coil with an active integrator circuit to reconstruct the incident current waveform from the coil voltage signal. A microcontroller-based processing unit records strike occurrences and classifies them by amplitude using predefined thresholds. Laboratory tests were carried out to evaluate the performance of the Rogowski coil and integrator circuit, validating the system accuracy in detecting current pulses associated with lightning strikes. Underway field tests will assess the sensor’s reliability during long-term autonomous operation on 345-kV transmission towers. The results demonstrate that the proposed system represents a practical and cost-effective solution for lightning monitoring in remote areas, contributing to enhanced data collection for engineering studies and improved reliability of electrical infrastructure. Full article

Regular inspection of overhead power line (OPL) systems is required to detect damage early and ensure the efficient and uninterrupted transmission of high-voltage electric power. In the past, these checks were conducted utilizing line crawling, inspection robots, and a helicopter. Yet, these traditional solutions are slow, costly, and hazardous. Advancements in drones, edge computing platforms, deep learning, and high-resolution cameras may enable real-time OPL inspections using drones. Some research has been conducted on OPL inspection with autonomous drones. However, it is essential to explore how to achieve real-time OPL component detection effectively and efficiently. In this paper, we report our research on OPL component detection on edge computing devices. The original OPL dataset is generated in this study. In this paper, we evaluate the detection performance with several sizes of training datasets. We also implement simple data augmentation to extend the size of datasets. The performance of the YOLOv7 model is also evaluated on several edge computing platforms, such as Raspberry Pi 4B, Jetson Nano, and Jetson Orin Nano. The model quantization method is used to improve the real-time performance of the detection model. The simulation results show that the proposed YOLOv7 model can achieve mean average precision (mAP) over 90%. While the hardware evaluation shows the real-time detection performance can be achieved in several circumstances. Full article

Overhead power lines are important components of power grids, and the status of transmission line equipment directly affects the safe and reliable operation of power grids. In order to guarantee the reliable operation of lines and efficient usage of the power grid, the tension of overhead power is an important parameter to be measured. The tension of power lines can be calculated from the modal frequency, but the measured acceleration data obtained from the accelerometer is severely contaminated with noises. In this paper, a multiscale-based peak detection (M-AMPD) algorithm is used to find possible modal frequencies in the power spectral density of acceleration data. To obtain a reliable noise-free signal, median absolute deviations with baseline correction (MAD-BS) algorithm are applied. An accurate estimation of modal frequencies used for tension estimation is obtained by iteration of the MAD-BS algorithm and reduction in frequency range technique. The iterative range reduction technique improves the accuracy of the estimated tension of overhead power lines. An accurate estimation of overhead power line tension can contribute to improving the reliability and efficiency of the power grid. The proposed algorithm is implemented in MATLAB R2020a and verified by comparison with measured data by a tensiometer. Full article

As an important means to ensure the safety of power transmission, the inspection of overhead transmission lines requires high accuracy for detecting small objects on the transmission lines and relies heavily on the construction of large-scale datasets by using deep learning instead of manual inspection. However, transmission inspection data often involve some sensitive information and need to be labeled by professionals, so it is difficult to construct a large transmission inspection dataset. In order to solve the problem of how to effectively train only on a small amount of transmission line data and achieve high object detection accuracy considering the large-scale variation in transmission objects, we propose an enhanced self-supervised pre-training model for DETR-like models, which are innovative object detectors eliminating hand-crafted non-maximum suppression and manual anchor design compared to previous CNN-based detectors. This paper mainly covers the following two points: (i) We compare UP-DETR and DETReg, noting that UP-DETR’s random cropping method performs poorly on small datasets and affects DETR’s localization ability. To address this, we adopt DETReg’s approach, replacing Selective Search with Edge Boxes for better results. (ii) To tackle large-scale variations in transmission inspection datasets, we propose a multi-scale feature reconstruction task, aligning feature embeddings with multi-scale encoder embeddings, and enhancing multi-scale object detection. Our method surpasses UP-DETR DETReg with DETR variants when fine-tuning PASCAL VOC and PTL-AI Furnas for object detection. Full article

This study proposes an autonomous recognition and approach method for unmanned aerial vehicles (UAVs) targeting linear splicing sleeves. By integrating deep learning and active stereo vision, this method addresses the navigation challenges faced by UAVs during the identification, localization, and docking of splicing sleeves on overhead power transmission lines. First, a two-stage localization strategy, LC (Local Clustering)-RB (Reparameterization Block)-YOLO (You Only Look Once)v8n (OBB (Oriented Bounding Box)), is developed for linear target splicing sleeves. This strategy ensures rapid, accurate, and reliable recognition and localization while generating precise waypoints for UAV docking with splicing sleeves. Next, virtual reality technology is utilized to expand the splicing sleeve dataset, creating the D_SS dataset tailored to diverse scenarios. This enhancement improves the robustness and generalization capability of the recognition model. Finally, a UAV approach splicing sleeve (UAV-ASS) visual navigation simulation platform is developed using the Robot Operating System (ROS), the PX4 open-source flight control system, and the GAZEBO 3D robotics simulator. This platform simulates the UAV’s final approach to the splicing sleeves. Experimental results demonstrate that, on the D_SS dataset, the RB-YOLOv8n(OBB) model achieves a mean average precision (mAP0.5) of 96.4%, with an image inference speed of 86.41 frames per second. By incorporating the LC-based fine localization method, the five rotational bounding box parameters (x, y, w, h, and angle) of the splicing sleeve achieve a mean relative error (MRE) ranging from 3.39% to 4.21%. Additionally, the correlation coefficients (ρ) with manually annotated positions improve to 0.99, 0.99, 0.98, 0.95, and 0.98, respectively. These improvements significantly enhance the accuracy and stability of splicing sleeve localization. Moreover, the developed UAV-ASS visual navigation simulation platform effectively validates high-risk algorithms for UAV autonomous recognition and docking with splicing sleeves on power transmission lines, reducing testing costs and associated safety risks. Full article

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption. Full article

In the field of smart grid monitoring, real-time defect detection for overhead transmission lines is crucial for ensuring the safety and stability of power systems. This paper proposes a defect detection model for overhead transmission lines based on an improved YOLOv8n model, named ABG-YOLOv8n. The model incorporates four key improvements: Lightweight convolutional neural networks and spatial–channel reconstructed convolutional modules are integrated into the backbone network and feature fusion network, respectively. A bidirectional feature pyramid network is employed to achieve multi-scale feature fusion, and the ASFF mechanism is used to enhance the sensitivity of YOLOv8n’s detection head. Finally, comprehensive comparative experiments were conducted with multiple models to validate the effectiveness of the proposed method based on the obtained prediction curves and various performance metrics. The validation results indicate that the proposed ABG-YOLOv8n model achieves a 4.5% improvement in mean average precision compared to the original YOLOv8n model, with corresponding increases of 3.6% in accuracy and 2.0% in recall. Additionally, the ABG-YOLOv8n model demonstrates superior detection performance compared to other enhanced YOLO models. Full article

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. Full article

The overhead line (OHL)–cable hybrid transmission line, which connects floating photovoltaic (PV) power plants, needs to be considered regarding whether to block reclosing operations or not. However, due to the weak-feed characteristics of PV inverters, existing methods are difficult to apply in this scenario. This paper proposes a criterion for fault section identification in the transmission lines of floating PV power plants based on traveling wave power and the zero-sequence impedance angle. Firstly, the fault current characteristics of photovoltaic inverters under dual-vector control are analyzed, and the applicability of the sequence component impedance directional criterion in this scenario is discussed. Then, the transmission, refraction, and reflection processes of traveling waves in OHL–cable hybrid lines are analyzed, and a traveling wave energy criterion is designed to determine the fault section. Finally, based on the scope of application of the zero-sequence impedance angle and traveling wave energy criterion, a fault section identification method for the hybrid lines of floating PV power plants is established. A deployment method for the proposed method, based on feeder terminal units (FTUs) at the connection points between the OHL and cable is proposed. This method identifies fault sections through traveling waves and zero-sequence impedance angles, which are unaffected by PV week feed characteristics, can be applied to all the AC fault types, and do not rely on multi-terminal synchronous sampling. The proposed method is verified on a 1MW PV system built in the PSCAD. Full article

This study investigates the impact of meteorological conditions on unplanned outages of overhead transmission lines (OHTL) in Lithuania’s 0.4–35 kV power grid from January 2013 to March 2023. Data from the Lithuanian electricity distribution network operator and the Lithuanian Hydrometeorological Service were integrated to attribute outage events with weather conditions. A Bayesian change point analysis identified thresholds for these meteorological factors, indicating points at which the probability of outages increases sharply. The analysis reveals that wind gust speeds, particularly those exceeding 21 m/s, are significant predictors of increased outage rates. Precipitation also plays a critical role, with a 15-fold increase in the relative number of outages observed when 3 h accumulated rainfall exceeds 32 mm, and a more than 50-fold increase for 12 h snowfall exceeding 22 mm. This study underscores the substantial contribution of lightning discharges to the number of outages. In forested areas, the influence of meteorological conditions is more significant. Furthermore, the research emphasizes that combined meteorological factors, such as strong winds accompanied by rain or snow, significantly increase the risk of outages, particularly in these forested regions. These findings emphasize the need for enhanced infrastructure resilience and targeted preventive measures to mitigate the impact of extreme weather events on Lithuania’s power grid. Full article

Overhead transmission line insulators are non-conductive materials that separate conductors from grounded transmission towers. Once in operation, they frequently experience environmental pollution and electrical or mechanical stress. Since adverse operational conditions can lead to insulation failure, regular inspections are essential to prevent power outages. To this end, this paper proposes a novel technique based on deep convolutional neural networks (CNNs) to classify high-voltage insulator surface conditions based on their image. Successful applications of CNNs in computer vision have led to several pretrained architectures for image classification. To use these pretrained models, a practitioner typically fine-tunes and selects one final model via a model selection stage and discards all other models. In contrast with many existing studies that use such a “winner-takes-all” approach, here, we identify the best subset of seven popular pretrained CNN architectures that are combined by soft voting to form an ensemble classifier. From a machine learning (ML) perspective, this focus is warranted because the convolutional base of each pretrained architecture operates as a feature extractor and an ensemble of them works as a combination of various feature extraction rules. Our numerical experiments demonstrate the advantage of the identified ensemble model to individual pretrained architectures. Full article