Line Insulators Research Articles

Detection of overhead line glass insulator condition using dual function device and deep learning approach

This paper presents a design of a multifunction smart wireless device for online condition monitoring of transmission line insulators. The proposed device can measure the insulator leakage current and take images of the high-voltage insulation. Yolov5-based models and deep convolutional neural networks (DCCN) are developed to analyze and classify the measured data and estimate the insulator's health condition. We have developed and tested a prototype of the proposed device. The device can issue a real-time warning message when a sudden change takes place in the leakage current value. The control center or smartphones receive the collected data wirelessly. We analyze the transmitted data using the developed methods to detect any anomalies and take appropriate remedial action. The performance and feasibility of the developed device are assessed through extensive experimental analysis. Results attest to the robustness of the proposed device, which is easy to install for existing and future overhead transmission line insulators.

A comparative analysis of an aerosol dry deposition microscale model for overhead powerlines insulators

Aerosol particles deposition on overhead power line insulators may significantly threaten electric systems, affecting dielectric properties and potentially causing surface discharges and service interruptions, leading to economic consequences. This study addresses this challenge by evaluating a dedicated microscale deposition model that integrated into an advanced air quality (AQ) modeling system. The microscale model integration into the AQ system aims to improve predictions of equivalent salt deposit density (ESDD), a key measure of insulator contamination. This enhancement is crucial for improving system resilience and reliability. The microscale model’s performance in reconstructing particulate matter (PM) deposition fluxes onto XP-70 porcelain-disk electrical insulator is assessed against measured ESDD data collected across various regions in Italy during several experimental campaigns. In order to better understand the specific phenomena impacting PM deposition, the microscale model is compared to the state-of-the-art Zhang deposition model used in CAMx. The analysis reveals that the microscale model tends to overestimate measured ESDD values and predicts higher deposition velocities than the Zhang model. These results suggest potential modelling differences between the two models, including variations in obstacle parameterizations, influence of particle aerodynamics, and dispersion modelling in the near-wall region.

Performance of insulators under variation of pollution, inclined angle, and temperature based on the design of experiment

The environmental conditions significantly impact the performance of the transmission line insulators. The current work investigated some variables that influence the flashover and breakdown voltages, such as insulator pollution (P), insulator inclined angles with the cross-arm (A), and insulator temperature (T). A prediction equation can be developed using Box-Behnken Design (BBD) to relate the input variables (P, A, and T) and the flashover voltage (VFO). BBD is a rotatable second-order design requiring three levels of each variable, influencing a specific experiment response. A statistical analysis was carried out to identify the significant variables affecting voltage VFO. So, the main objective of the work is to investigate the effect of P, A, and T on the VFO and use Box Behnken Design (BBD) to predict the value of VFO when the information about P, A, and T is available without requiring any experimental works. The results illustrated a prediction equation that facilitates the prediction of the voltage VFO if new values of the input parameters are known. The prediction error of the constructed equation was less than 5 % for a total number of 21 new data samples used to test and verify the robustness of the prediction equation.

SODD-YOLOv8: an insulator defect detection algorithm based on feature enhancement and variable row convolution

Abstract To address the detection challenges in defective images of insulators in transmission lines, including tiny object size, significant scale variations, a wide variety of defects, and complex background interference. In this study, an improved insulator defect detection algorithm is proposed, based on the YOLOv8s framework and combining feature enhancement and deformable convolution techniques. Firstly, to address the image feature distortion problem caused by aerial photography, a deformable convolutional feature extraction module (DCFEM) is introduced, which is designed to enhance the model’s ability to adapt to the local geometric deformation, so as to effectively recover the distorted feature information in the image. Moreover, to enhance the detection ability of the model for small objects, a small object feature enhancement module is designed, which adopts an efficient multi-scale attention mechanism, and aims to enhance the feature extraction ability of small objects, improve the sensitivity to small-size defects, and improve the detection accuracy. Eventually, to optimize the computational efficiency of the model, the average pooling-sparse convolution-batch normalization (BN) module is proposed. This module combines average pooling, sparse convolution and BN techniques to achieve a lightweight model while maintaining a high level of feature extraction capability. Experimental results on the China power line insulator dataset show that the improved model achieves a 4.3 percentage point improvement in the mAP metric compared to YOLOv8s, and the number of parameters in the model is reduced by 10%. The proposed scheme not only improves the accuracy and efficiency of defect detection, but also reduces the demand for computational resources, thus providing a more reliable and efficient solution for insulator defect detection in practical applications.

Prototype for Multi-UAV Monitoring–Control System Using WebRTC

Most unmanned aerial vehicle (UAV) ground control station (GCS) solutions today are either web-based or native applications, primarily designed to support a single UAV. In this paper, our research aims to provide an open, universal framework intended for rapid prototyping, addressing these objectives by developing a Web Real-Time Communication (WebRTC)-based multi-UAV monitoring and control system for applications such as automated power line inspection (APOLI). The APOLI project focuses on identifying damage and faults in power line insulators through real-time image processing, video streaming, and flight data monitoring. The implementation is divided into three main parts. First, we configure UAVs for hardware-accelerated streaming using the GStreamer framework on the NVIDIA Jetson Nano companion board. Second, we develop the server-side application to receive hardware-encoded video feeds from the UAVs by utilizing a WebRTC media server. Lastly, we develop a web application that facilitates communication between clients and the server, allowing users with different authorization levels to access video feeds and control the UAVs. The system supports three user types: pilot/admin, inspector, and customer. Our research aims to leverage the WebRTC media server framework to develop a web-based GCS solution capable of managing multiple UAVs with low latency. The proposed solution enables real-time video streaming and flight data collection from multiple UAVs to a server, which is displayed in a web application interface hosted on the GCS. This approach ensures efficient inspection for applications like APOLI while prioritizing UAV safety during critical scenarios. Another advantage of the solution is its integration compatibility with platforms such as cloud services and native applications, as well as the modularity of the plugin-based architecture offered by the Janus WebRTC server for future development.

Application of End-to-End Perception Framework Based on Boosted DETR in UAV Inspection of Overhead Transmission Lines

As crucial predecessor tasks for fault detection and transmission line inspection, insulators, anti-vibration hammers, and arc sag detection are critical jobs. Due to the complexity of the high-voltage transmission line environment and other factors, target detection work on transmission lines remains challenging. A method for high-voltage transmission line inspection based on DETR (TLI-DETR) is proposed to detect insulators, anti-vibration hammers, and arc sag. This model achieves a better balance in terms of speed and accuracy than previous methods. Due to environmental interference such as mountainous forests, rivers, and lakes, this paper uses the Improved Multi-Scale Retinex with Color Restoration (IMSRCR) algorithm to make edge extraction more robust with less noise interference. Based on the TLI-DETR’s feature extraction network, we introduce the edge and semantic information by Momentum Comparison (MoCo) to boost the model’s feature extraction ability for small targets. The different shooting angles and distances of drones result in the target images taking up small proportions and impeding each other. Consequently, the statistical profiling of the area and aspect ratio of transmission line targets captured by UAV generate target query vectors with prior information to enable the model to adapt to the detection needs of transmission line targets more accurately and effectively improve the detection accuracy of small targets. The experimental results show that this method has excellent performance in high-voltage transmission line detection, achieving up to 91.65% accuracy and a 55FPS detection speed, which provides a technical basis for the online detection of transmission line targets.

Impact of atmospheric conditions on the flash-over voltage of the transmission line insulators using central composite design

The insulators of overhead power lines play a crucial role in maintaining the reliability of transmission and distribution networks. Because they are exposed to harsh and dynamic environmental conditions, it is essential to investigate the impact of environmental parameters such as pollution, inclined angle with the cross arm, and temperature on the dielectric performance of the insulators of overhead lines. Conventionally, the effect of such parameters can be investigated through experimental measurements of the insulator flashover voltage. However, this approach is costly and time-consuming and calls for the isolation of the lines to conduct the test, causing interruption to the entire grid. As such, there is an essential need to develop a new methodology to quantify the flashover voltage of overhead insulators operating under various environmental conditions, which is the main aim of this paper. The Central Composite Design is employed to develop a mathematical correlation between the insulator flash over voltage as a dependent variable and three environmental parameters: pollution level, inclined angle, and temperature as independent variables. The robustness of the developed equation is validated through extensive experimental measurements of the insulator’s flash overvoltage under various conditions. Results reveal a good agreement between the actual and predicted flashover voltage using the developed correlation, as the absolute error for all investigated samples is less than 6%.

Investigation of Impedance Determination using Two-Point High Frequency Injection Technique for Non-Contact Voltage Measurement System

Energy management facilities are becoming more necessary due to the growth of smart energy and microgrid technologies. Convenient access to information on energy consumption is an advantage for energy planning and management and secure for the operators. Consequently, these conceive the idea of developing non-contact sensor approach to replace the conventional energy measurement while maintain full accessibility to voltage and current information. This paper proposes a method of non-contact conductive voltage measurement with an automated calibration system. The previous application of non-contact voltage measurement systems in the low-voltage range used a method based on the capacitive coupling principle by covering a metal plate around an insulated power line cable. The important factor of measurement accuracy depends on the determination of impedance occurring between the conductors within the cable and the metal plates attached to the cable surface by insulating as a dielectric. The proposed impedance determination process employs a two-point high-frequency signal injection technique that transmits a reference signal to calculate the established parasitic impedance value. From the high-frequency injection technique, the resulting impedance can be calibrated throughout the reverse calculation to measure the actual voltage of the power line. The experimental results of the proposed voltage measurement system can measure the actual voltage of the power line with the capability to calibrate the impedance due to the variation in the environment and eliminate noise errors from the instability under various conditions.

Insulator Defect Detection Based on YOLOv5s-KE

To tackle the issue of low detection accuracy in insulator images caused by intricate backgrounds and small defect sizes, as well as the requirement for real-time detection on embedded and mobile devices, this research introduces the YOLOv5s-KE model. Integrating multiple strategies, YOLOv5s-KE aims to boost detection accuracy significantly. Initially, an enhanced anchor generation method utilizing the K-means++ algorithm is proposed to generate more appropriate anchor boxes for insulator defects. Moreover, an attention mechanism is integrated into both the backbone and neck networks to enhance the model’s capacity to focus on defect features and resist interference. To improve the detection of small defects, the EIoU loss function is implemented in place of the original CIoU loss function. In order to meet the real-time detection needs on embedded and mobile devices, the model is further refined through the integration of Ghost convolution for lightweight feature extraction and a linear transformation to reduce the computational burden of standard convolution. A channel pruning strategy is deployed to optimize the sparsely trained network, diminishing redundancy, and improving model generalization. Additionally, the CARAFE operator replaces the original upsampling operator to minimize model parameters and elevate detection speed. Experimental outcomes demonstrate that YOLOv5s-KE achieves a detection accuracy of 92.3% on the Chinese transmission line insulator dataset, marking a 5.2% enhancement over the original YOLOv5s. The streamlined version of YOLOv5s-KE achieves a detection speed of 94.3 frames per second, indicating an improvement of 30.1 frames per second compared to the original model. Model parameters are condensed to 9.6 M, resulting in a detection accuracy of 91.1%. This study underscores the precision and efficiency of the proposed approach, suggesting that the advanced strategies explored introduce novel possibilities for insulator defect detection.

Investigation of distribution system modification using operation voltage unification in order to decrease interruptions in dust climate

Many events in power networks which caused failure and outage of subsystems have root in design and initial selection of equipment and their specification. This concern will be important while the any kind of expanding and acclimation plan need to be executed to strengthen the existing infrastructures in order to provide better condition with regard to satisfied criteria. In Iran one of the most adverse environmental occurrences known Fine Dust, may force several distribution utilities to be changed due to new technical requirements. Medium voltage level regrading in way to decrease or unification of distribution system have been proposed in this paper to reach reduced faults and fast replacement of equipment in damaged situations. Voltage level changing and selection have been investigated in order to increase the network strengthen and reselection of equipment and redesigning critical sections of system against natural events that called fine dust with aim to attain an upgraded distribution utility. Operating of existing 33 kV and 11 kV network in 20 kV level and investigation of changing process have been done for Ahvaz medium voltage distribution system as a case study in these paper's sections. Calculations and results will be contained maximum current capacities in different line conductors, insulation and air clearances assessment, poles acceptable mechanical values, short circuit current and protection coordination for changing voltage level in distribution system.

A Lightweight Insulator Defect Detection Model Based on Drone Images

With the continuous development and construction of new power systems, using drones to inspect the condition of transmission line insulators has become an inevitable trend. To facilitate the deployment of drone hardware equipment, this paper proposes IDD-YOLO (Insulator Defect Detection-YOLO), a lightweight insulator defect detection model. Initially, the backbone network of IDD-YOLO employs GhostNet for feature extraction. However, due to the limited feature extraction capability of GhostNet, we designed a lightweight attention mechanism called LCSA (Lightweight Channel-Spatial Attention), which is combined with GhostNet to capture features more comprehensively. Secondly, the neck network of IDD-YOLO utilizes PANet for feature transformation and introduces GSConv and C3Ghost convolution modules to reduce redundant parameters and lighten the network. The head network employs the YOLO detection head, incorporating the EIOU loss function and Mish activation function to optimize the speed and accuracy of insulator defect detection. Finally, the model is optimized using TensorRT and deployed on the NVIDIA Jetson TX2 NX mobile platform to test the actual inference speed of the model. The experimental results demonstrate that the model exhibits outstanding performance on both the proprietary ID-2024 insulator defect dataset and the public SFID insulator dataset. After optimization with TensorRT, the actual inference speed of the IDD-YOLO model reached 20.83 frames per second (FPS), meeting the demands for accurate and real-time inspection of insulator defects by drones.

Analysis of thermal insulation effect of off-wall insulation lining in high-temperature tunnel

Most prior studies on the insulation effect of off-wall insulation have neglected the natural convection of the air lining; this assumption is inconsistent with reality. In this study, the effect of natural air convection on the heat transfer mechanism in off-wall insulation lining was studied via experiments and numerical simulation. The thermal insulation effect of the air lining was evident. The temperature field distribution of the surrounding rock is affected by the natural convection in the air lining and is thus clearly related to the azimuth. The temperature disturbance of the surrounding rock in the direction of θ = 0° is the minimum, whereas that in the direction of θ = 180° is the maximum. The most significant factors affecting the thermal insulation effect of the off-wall insulation lining are the thermal conductivity of the rock and thermal resistance of the insulation layer, followed by thickness of the air lining and temperature difference between the original rock temperature and the air flow. The relationships between the average unstable heat transfer coefficient and various parameters were obtained via fitting. The fitting formula can be used in engineering practice to calculate the heat transfer from the surrounding rock to the tunnel airflow under various conditions.

Research On Transmission Line Insulator Defect Detection Method Based on Yolov8

Research On Transmission Line Insulator Defect Detection Method Based on Yolov8

Evaluation of the backflashover performance of 150 kV and 400 kV double-circuit overhead transmission lines as affected by lightning attachment models and peak current distributions

The evaluation of the backflashover performance of overhead transmission lines requires the adoption of a lightning attachment model and a peak current distribution. These affect the estimated backflashover rate, BFR, of the lines. The present study aims to quantify the influence of lightning attachment model and peak current distribution on the BFR of double-circuit overhead lines. This is achieved by evaluating typical 150 kV and 400 kV lines. ATP-EMTP simulations are conducted to obtain the critical currents causing backflashover of line insulation. Then, the critical currents are used in lightning incidence computations to obtain BFR. The influencing parameters taken into account in this work are the low current and low frequency tower ground resistance, the location of lightning strike to the shield wire along the span, and the AC voltage phase angle, all affecting the critical backflashover current. Several lightning attachment models and peak current distributions are evaluated. Their influence on BFR is found to be considerable, especially for lower tower ground resistance values. The lightning peak current distributions yield more pronounced effects on BFR. The results of this study stress the need for computing BFR using a more accurate expression than the simplified one based on CIGRE TB 63.

Application of sulfur hexafluoride in electrical engineering. Historical past, current state and prospects in future

A pressing issue today is the analysis of the possibilities of using SF6 gas and its possible limitations of use from the point of view of harmful effects on the environment. The article, based on free sources of information, systematizes the historical facts of the development of SF6 gas, the main stages of the formation of the SF6 gas environment as insulation for electrical equipment. Comparative characteristics of electrical strength for various insulating substances are presented. Research has been carried out on modern examples of the use of SF6 gas in medium and high voltage circuit breakers. Examples of the use of SF6 gas and its mixtures as gas insulation for high-voltage power lines are analyzed. It is shown that a promising direction for the development of SF6 gas is its use in gas-insulated power lines. Design features and examples of practical application of high-voltage gas-insulated power lines are presented. The features of using SF6 insulation in electrical equipment of different voltage classes are determined. Current environmental problems associated with the use of SF6 gas are analyzed and possible directions for overcoming them are established. The elements of SF6 gas decomposition products that are toxic and pose the greatest danger are considered. The characteristics of SF6 gas as a greenhouse gas are given and promising ways to reduce its use are shown. It has been established that by using not pure SF6 gas, but gas mixtures based on it, it is possible to significantly reduce the percentage of SF6 gas use. Promising directions for further development of gas insulating medium for electrical equipment without the use of SF6 gas have been established.

The Potential of Recycling and Reusing Waste Materials in Underground Construction: A Review of Sustainable Practices and Challenges

Underground infrastructure projects pose significant environmental risks due to resource consumption, ground stability issues, and potential ecological damage. This review explores sustainable practices for mitigating these impacts throughout the lifecycle of underground construction projects, focusing on recycling and reusing excavated tunnel materials. This review systematically analyzed a wide array of sustainable practices, including on-site reuse of excavated tunnel material as backfill, grouting, soil conditioning, and concrete production. Off-site reuses explored are road bases, refilling works, value-added materials, like aggregates and construction products, vegetation reclamation, and landscaping. Opportunities to recover and repurpose tunnel components like temporary support structures, known as “false linings”, are also reviewed. Furthermore, the potential for utilizing industrial and construction wastes in underground works are explored, such as for thermal insulation, fire protection, grouting, and tunnel lining. Incorporating green materials and energy-efficient methods in areas like grouting, lighting, and lining are also discussed. Through comprehensive analysis of numerous case studies, this review demonstrates that with optimized planning, treatment techniques, and end-use selection informed by material characterization, sustainable practices can significantly reduce the environmental footprint of underground infrastructure. However, certain approaches require further refinement and standardization, particularly in areas like the consistent assessment of recycled material properties and the development of standardized guidelines for their use in various applications. These practices contribute to broader sustainability goals by reducing resource consumption, minimizing waste generation, and promoting the use of recycled and green materials. Achieving coordinated multi-stakeholder adoption, including collaboration between contractors, suppliers, regulatory bodies, and research institutions, is crucial for maximizing the impact of these practices and accelerating the transition towards a more sustainable underground construction industry.

Field Natural Experimental Study of Ice Growth Characteristics of Split Conductor and Single Conductor on Hybrid Freezing Icing

Abstract Transmission line icing seriously endangers the safe operation of transmission lines. At present, most of the research on transmission line icing is based on numerical simulation, and the growth law of transmission line ice in a natural environment has not been explored and studied. In this paper, natural ice-covering experiments are carried out in the Xuefeng Mountain Field (Natural) Ice Covering Test Base of Chongqing University, and single conductor, three-split, and four-split conductor ice-covering change rules in the mixed rime ice environment are explored. Test results found that the wind direction has a greater impact on the ice, and the windward side of the conductor ice is much larger than the leeward side. The downwind side of the ice of both single conductor and split conductor is smaller than the upwind side. The weight of ice on the split conductor line on the downwind side is about 86% of that on the upwind side. The results can provide a reference for the design of external insulation of UHV DC transmission lines.

Analysis and Optimization of Lightning Protection Level Against Backflash for 220 kV Transmission Lines

Abstract Lightning strikes are a major cause of insulator flashover and line tripping; hence, improving line lightning resistance is essential. This article establishes a 220 kV transmission line model in PSCAD/EMTDC and examines the effect of alternative grounding resistance and phase arrester configurations on lightning resistance levels. The analytical findings demonstrate that the transmission line’s lightning resistance level is inversely connected with its ground resistance. When ground resistance cannot be lowered, lightning resistance can be improved by adding a lightning arrester. The lightning resistance level of each arrester arrangement varies depending on the ground resistance circumstances. An appropriate configuration policy must be picked depending on the site characteristics. The study results in this research give a specific reference for lightning protection transformation of 220 kV transmission lines.

SnakeNet: An adaptive network for small object and complex background for insulator surface defect detection

Aiming at the current problems of small-target defect detection difficulty, weak resistance to complex background interference, and low detection precision in insulator defect detection, an insulator defect detection model, SnakeNet, is proposed based on the YOLOv8 detection framework. Firstly, the Dynamic Snake Convolutional Neck (DSConv-Neck) is proposed to extract rich fine-grained features and improve the detection ability of small-target defects. Then, the background interference is suppressed by the C2f Efficient Multi-Scale Attention (C2MA) module to enhance the anti-background resistance; finally, the GhostConv VanillaNet (GCVNet) module is proposed to minimize the number of model parameters while ensuring the detection precision. Many experiments are carried out on two public datasets, the China power line insulator dataset (CPLID) and the Insulator Defect Image Dataset (IDID). The results show that the optimal detection precision and recall of SnakeNet are 98.7% and 98.8%, respectively, and the number of model parameters and computation volume are only 2.9M and 6.7G. Compared with the original YOLOv8, the detection precision and recall are improved by 2.4% and 3.5%, respectively, and the number of model parameters and computation volume are reduced by 0.3M and 1.3M, respectively. Compared with other algorithms, such as SSD and RT-DETR, SnakeNet performs well in detecting small targets and adapting to complex backgrounds with lower computational costs.

A bird droppings flashover analysis method based on the combination of electric field distortion and spatial electric field distribution

To study the flashover of air gap caused by bird droppings shorting air gap and the influence of space electric field change on it, the distortion electric field simulation, space electric field simulation, and related flashover test of transmission line bird droppings falling process were carried out. By analyzing the specific influencing factors of bird droppings flashover fault, it is pointed out that the electric field distortion caused by bird droppings will affect the spatial electric field distribution around the insulator and thus affect the air gap flashover. This study analyzes the specific causes of bird dropping flashover of transmission line insulators, reduces the number of bird damage trips of transmission lines, reduces the cost of operation and maintenance of transmission lines, improves the intrinsic safety of transmission lines, and ensures the safe and reliable operation of power grids.