Power Line Monitoring Research Articles

APF-YOLOV8: Enhancing Multiscale Detection and Intra-Class Variance Handling for UAV-Based Insulator Power Line Inspections

Background UAV-based power line inspections offer a safer, more efficient alternative to traditional methods, but insulator detection presents key challenges: multiscale object detection and intra-class variance. Insulators vary in size due to UAV altitude and perspective changes, while their visual similarities across types (e.g., glass, porcelain, composite) complicate classification. Methods To address these issues, we introduce APF-YOLO, an enhanced YOLOv8-based model integrating the Adaptive Path Fusion (APF) neck and the Adaptive Feature Alignment Module (AFAM). AFAM balances fine-grained detail extraction for small objects with semantic context for larger ones through local and global pathways by integrating advanced attention mechanisms. This work also introduces the Merged Public Insulator Dataset (MPID), a comprehensive dataset designed for insulator detection, representing diverse real-world conditions such as occlusions, varying scales, and environmental challenges. Results Evaluations on MPID demonstrate that APF-YOLO surpasses state-of-the-art models with different neck configurations, achieving at least a +2.71% improvement in mAP@0.5:0.9 and a +1.24% increase in recall, while maintaining real-time performance in server-grade environments. Although APF-YOLO adds computational requirements, these remain within acceptable limits for real-world applications. Future work will optimize APF-YOLO for edge devices through techniques such as model pruning and lightweight feature extractors, enhancing its adaptability and efficiency. Conclusion Combined with MPID, APF-YOLO establishes a strong foundation for advancing UAV-based insulator detection, contributing to safer and more effective power line monitoring.

A UAV Intelligent System for Greek Power Lines Monitoring.

Power line inspection is one important task performed by electricity distribution network operators worldwide. It is part of the equipment maintenance for such companies and forms a crucial procedure since it can provide diagnostics and prognostics about the condition of the power line network. Furthermore, it helps with effective decision making in the case of fault detection. Nowadays, the inspection of power lines is performed either using human operators that scan the network on foot and search for obvious faults, or using unmanned aerial vehicles (UAVs) and/or helicopters equipped with camera sensors capable of recording videos of the power line network equipment, which are then inspected by human operators offline. In this study, we propose an autonomous, intelligent inspection system for power lines, which is equipped with camera sensors operating in the visual (Red-Green-Blue (RGB) imaging) and infrared (thermal imaging) spectrums, capable of providing real-time alerts about the condition of power lines. The very first step in power line monitoring is identifying and segmenting them from the background, which constitutes the principal goal of the presented study. The identification of power lines is accomplished through an innovative hybrid approach that combines RGB and thermal data-processing methods under a custom-made drone platform, providing an automated tool for in situ analyses not only in offline mode. In this direction, the human operator role is limited to the flight-planning and control operations of the UAV. The benefits of using such an intelligent UAV system are many, mostly related to the timely and accurate detection of possible faults, along with the side benefits of personnel safety and reduced operational costs.

Energy Harvesting Device for Smart Monitoring of MV Overhead Power Lines-Theoretical Concept and Experimental Construction.

Modern technological advancements have opened avenues for innovative low-energy sources in construction, with electric field energy harvesting (EFEH) from overhead power lines serving as a prime candidate for empowering intelligent monitoring sensors and vital communication networks. This study delves into this concept, presenting a physical model of an energy harvester device. The prototype was meticulously designed, simulated, constructed, and tested, to validate its foundational mathematical model, with implications for future prototyping endeavors. The findings illustrate the potential of harnessing ample power from this device when deployed on medium-voltage (MV) overhead power lines, facilitating the monitoring of electric and meteorological parameters and their seamless communication through the Internet of Things (IoT) network. The study focused on the medium voltage applications of the harvester. Two dielectric materials were tested in the present experiments: air and polyurethane. The measurement results exhibited satisfactory alignment, particularly with the air dielectric. Nevertheless, deviations arose when employing polyurethane rubber as the dielectric, due to impurities and defects within the material. The feasibility of generating the requisite 0.84 mW output power to drive process electronics, sensors, and IoT communications was established. The novelty of this work rests in its comprehensive approach, cementing the theoretical concept through rigorous experimentation, and emphasizing its application in enhancing the efficacy of overhead power line monitoring.

AAM/goods delivery: main enablers for BVLOS routine operations within environment at low and medium risk

PurposeTheadvanced air mobility (AAM) is defined by National Aeronautics and Space Administration (NASA) as safe, accessible, automated and affordable air transportation system for passengers and cargo, capable of serving previously hard-to-reach urban and rural sites. The purpose of this paper is to focus on explaining potential solutions, under study by the authors, which could support beyond visual line of sight (BVLOS) operations for goods delivery in a safe way.Design/methodology/approachAccording to recent NASA-commissioned market studies, by 2030, there will be as many as 500 million flights a year for package delivery services and 750 million flights a year for passengers’ transportation (AAM). A significant number of these aircrafts will be unmanned aerial vehicles, meaning that they are self-flying or autonomous, of which the smallest ones are quadcopters: they are relatively inexpensive and are capable to perform various tasks, such as aerial observation, crop monitoring and treatment, search and rescue, power line monitoring and goods delivery. On the other hand, there are still many difficulties in introducing them into medium- and low-risk BVLOS routine operations for goods delivery: unfortunately, there are no regulations and technologies yet that enable these operations.FindingsThis conceptual paper outlines the studies about possible solutions, identified by authors, which could support BVLOS operations in a medium- and low-risk environment; in particular, the following aspects have been analysed: regulations, integrating control systems for drones, sensors (on board obstacle detection and avoidance), emergency management (emergency on ground system to identify safe landing areas), concepts of droneway (or flight corridors) and drones recovery hub.Originality/valueThe purpose of this paper is to provide a conceptual description of the possible solutions, under study by the authors, which could contribute enabling the BVLOS operations in a medium- and low-risk environment. The paper aims describing the state of the art, terms of regulations, classifications and limitations and describing possible conceptual solutions that could guarantee safety in introducing unmanned aircraft system operations inside urban areas.

Linking digital image intensity to carrier density in low-pressure corona discharges

The electrification of transportation, and aircraft electrification in particular, is experiencing rapid development due to the more efficient use of energy. Since the dielectric strength of air decreases at the cruising altitudes of commercial aircraft due to the reduced pressure environment, there is a need to control and minimize the risks associated with electrical discharges. This paper shows from experimental and computational data that there is a relationship between the electrical and optical phenomena involved in the discharge process. To this end, this paper analyzes corona discharges generated using different electrode geometries under a wide range of pressures from 100 kPa to 10 kPa. It is shown that the densities of charged particles or charge carriers generated during the discharge process are positively correlated with the intensity of corona images acquired with a digital imaging sensor sensitive to the near UV and visible wavelength ranges. Therefore, the intensity of the images can be used as a reliable and accurate indicator of the corona activity, the values of which are related to the ionization processes involved in the discharges. The results presented in this paper can be applied in various physical and engineering fields, such as high voltage engineering, power line monitoring, or ozone generation, among others.

An electric-field high energy harvester from medium or high voltage power line with parallel line

Abstract In order to effectively monitor transmission lines and transmission towers, a number of different types of sensors are needed. A lot of times, these sensors along with the transmission lines and transmission towers are in inaccessible or hard-to-access areas and replacing their batteries is difficult. Yet they need consistent power supply. By harvesting energy directly from these medium and high voltage power lines, these devices can become self-sustaining while the overall system is more friendly to the environment. This paper presents a novel approach to high energy harvesting based on capacitive coupling between the power line and the harvesting line. This technique has several advantages, namely high output voltage, easy adjustment of coupling coefficient, and low cost. The validation and implantation of this harvesting system are proposed with the support of experimental results. This energy-harvesting ability of W and mA levels is achieved for the power line monitoring devices, with higher power output depending on the length and the size of the harvesting line.

Ultra-long chaotic FBG sensing with high-order random fiber lasing amplification.

We propose and demonstrate an ultra-long chaotic fiber Bragg grating (FBG) sensing system based on wavelength-scanning correlation optical time-domain reflectometry (COTDR) assisted by sixth-order random fiber lasing amplification (RFLA). Cascaded random Raman fiber lasing generated in the long fiber span can provide up to sixth-order distributed Raman amplification for the chaotic probe light and its echo signal without ruining the chaotic behavior, which can significantly extend the sensing distance of COTDR. As a result, a 152-km-long wavelength-scanning COTDR is experimentally demonstrated to simultaneously realize FBG sensing and location with a spatial resolution as high as 6 cm, which is the longest COTDR to the best of our knowledge. Temperature sensing of the specific FBG is performed, and the temperature sensitivity of the proposed system is 0.25 dB/°C with a good linearity. The proposed chaotic FBG sensing system with high-order RFLA can act as a new platform for ultra-long, large-capacity FBG sensing, which has potential applications in overhead transmission powerline monitoring and structural health monitoring.

Saving raptors in Mongolia: country-scale retrofitting of insulation to reduce avian electrocution at power lines

The delivery of electricity to widely dispersed communities across the open steppe grasslands of Mongolia depends on medium-voltage (10–35 kV) distribution lines, often carrying power over substantial distances. Previously utilizing wooden supports, upgraded and new distribution lines are now designed to be cheaper, using steel-reinforced concrete poles with galvanized steel crossarms that can carry an electrical charge to the ground. Consequently, any bird that perches on these poles and simultaneously touches a conducting cable can be electrocuted, which is a significant risk for medium to large-sized birds at 10 kV and 15 kV lines utilizing pin-insulators. Surveys and monitoring of these dangerous power lines revealed that raptor electrocution rates were high, especially in areas with abundant prey, and widespread across steppe habitats. With around 3,500 km of hazardous lines in the Mongolian steppe zone, it was estimated around 18,000 raptors, including 4,000 Saker Falcons, were electrocuted each year. Experimental trials indicated that retrofitting insulation could significantly reduce electrocution risk. However, off-the-shelf mitigation equipment was either not suitable or too expensive to be deployed at scale in Mongolia. Consequently, we designed and manufactured our own equipment to meet our six criteria of being (i) effective, (ii) durable, (iii) failsafe with (iv) no impact on power supply, (v) simple to install and (vi) low cost. This equipment comprises acrylonitrile styrene acrylate (ASA) insulation covers that fit over the crossarms and a concrete pole tops of standard line poles, suitable for retrofitting existing lines and being incorporated into new lines. Over to 34,425 poles at 67 power lines covering a linear distance of 3,450 km, at an equipment cost of less than USD $40 per pole. Comparison of carcass counts indicated a significant reduction in electrocution events. To our knowledge, this is the largest single powerline retrofitting project globally, demonstrating that effective mitigation can be deployed at scale, saving tens of thousands of raptors from electrocution for a relatively low cost. We hope to use this project as an exemplar to focus attention and attract funding to address the issue of raptor electrocution at a global scale.

Autonomous Flight of High-Endurance UAVs to Monitor Powerlines

In this project, the authors will develop a highly efficient UAV (unmanned aerial vehicle) architecture for long endurance flights. The authors propose an IoT (internet of things) model that will connect a network of drones using a 4G LTE (long-term evolution) connection. The researchers also propose the use of MEC (mobile edge computing) stations, smart GCSs (ground control stations) for battery replacement, and wireless charging via powerline magnetic field harvesting. The authors will be applying these ideas to a powerline monitoring mission profile, creating a large sensor network with computer vision capabilities to detect powerline faults and report them to the proper authorities.

A Real-Time Monitoring and Warning System for Power Grids Based on Edge Computing

It is very important for the safe operation of the power grid to be able to achieve real-time online monitoring, but along with the expansion of the smart grid, powerful computing power is required to achieve online monitoring, which cannot be loaded by ordinary servers. Due to the limited computing and storage resources of the server, it is difficult to fully meet the real-time requirements of online power line monitoring. Therefore, this research proposes a smart grid real-time monitoring and early warning system based on edge computing, which can reasonably allocate computing tasks according to the effective use of edge node resources. It can also collect and process information in real time, and can monitor the power grid online, which improves the fault identification efficiency of the power grid, effectively reduces the economic cost, and relieves the computing pressure of the online monitoring equipment on the cloud computing server. Meanwhile, considering the burst problem in allocation queue optimization, a multipriority allocation queue algorithm is proposed, and an improved greedy algorithm allocation model is used to solve the optimal scheduling problem between bursts. Simulation analysis shows that the scheduling scheme proposed in this paper can effectively reduce the monitoring delay of the power line monitoring system, improve the overall adaptability and compatibility of the system, meet the market demand, and facilitate promotion.

Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review

Recently, unmanned aerial vehicles (UAVs), also known as drones, have come in a great diversity of several applications such as military, construction, image and video mapping, medical, search and rescue, parcel delivery, hidden area exploration, oil rigs and power line monitoring, precision farming, wireless communication and aerial surveillance. The drone industry has been getting significant attention as a model of manufacturing, service and delivery convergence, introducing synergy with the coexistence of different emerging domains. UAVs offer implicit peculiarities such as increased airborne time and payload capabilities, swift mobility, and access to remote and disaster areas. Despite these potential features, including extensive variety of usage, high maneuverability, and cost-efficiency, drones are still limited in terms of battery endurance, flight autonomy and constrained flight time to perform persistent missions. Other critical concerns are battery endurance and the weight of drones, which must be kept low. Intuitively it is not suggested to load them with heavy batteries. This study highlights the importance of drones, goals and functionality problems. In this review, a comprehensive study on UAVs, swarms, types, classification, charging, and standardization is presented. In particular, UAV applications, challenges, and security issues are explored in the light of recent research studies and development. Finally, this review identifies the research gap and presents future research directions regarding UAVs.

Application of an Artificial Thunderstorm Cell to Study the Effect of Atmospheric Electricity and Lightning on the Elements Protected by Dielectric Shells in Overhead Power-Line Monitoring Systems

Application of an Artificial Thunderstorm Cell to Study the Effect of Atmospheric Electricity and Lightning on the Elements Protected by Dielectric Shells in Overhead Power-Line Monitoring Systems

Approximate Magnetic Flux Density Model of Overhead Power Lines for Conductor Sag Estimation

Monitoring and supervision of overhead power lines have become essential actions in Electrical Power Systems since the introduction of the Smart Grid concept. In order to monitor overhead power lines, simple non-invasive devices are mounted on support poles, which detect line abnormalities and faults by measuring magnetic field originating from power line conductors. Measured signals are also used to determine various electrical and non-electrical line parameters. In this paper a new approximate model for magnetic flux density vector components of the overhead power lines suitable for real-time conductor sag estimation is presented. In the proposed model, catenary-shaped power line conductors are approximated with tilted straight line conductors, and the approximate model is calibrated by tuning model coefficients to match flux density vector components as in the case of real catenary-shaped conductors. The accuracy of the proposed model is verified by measurements on three-phase overhead power line model scaled to laboratory conditions. As a conclusion, an adaptive method for possible practical implementation based on the approximate model is considered.

Математическая модель модуля дистанционного мониторинга проводов по каналу измерения температуры провода воздушной линии

In continuation of the previously published research results (Energy Safety and Energy Economy, iss. 2, 2021), we have developed wire temperature-based mathematical models for overhead power line monitoring. Mathematical models simulate allowable amperage load considering the temperature and an allowable wire sag considering the temperature and amperage. The proposed mathematical models became a base for overhead power line remote control tools being currently tested at power utility companies.

A Hierarchical Clustering Method to Repair Gaps in Point Clouds of Powerline Corridor for Powerline Extraction

Powerline detection is becoming a significant issue for powerline monitoring and maintenance, which further ensures transmission security. As an efficient method, laser scanning has attracted considerable attention in powerline detection for its high precision and robustness during the night period. However, due to occlusion and varying point density, gaps will appear in scans and greatly influence powerline detection by over–clustering, insufficient extraction, or misclassification in existing methods. Moreover, this situation will be worse in terrestrial laser scanning (TLS), because TLS suffers more from gaps due to its unique ground–based scanning mode compared to other laser scanning systems. Thereby, this paper explores a robust method to repair gaps for extracting powerlines from TLS data. Firstly, a hierarchical clustering method is used to extract the powerlines. During the clustering, gaps are repaired based on neighborhood relations of powerline candidates, and repaired gaps can create continuous neighborhood relations that ensure the execution of the clustering method in return. Test results show that the hierarchical clustering method is robust in powerline extraction with repaired gaps. Secondly, reconstruction is performed for further detection. Pylon–powerline connections are found by the slope change method, and powerlines with multi–span are successfully fitted using these connections. Experiment shows that it is feasible to find connections for multi–span reconstruction.

Fusion of CNN and sparse representation for threat estimation near power lines and poles infrastructure using aerial stereo imagery

Fires or electrical hazards and accidents can occur if vegetation is not controlled or cleared around overhead power lines, resulting in serious risks to people and property and significant costs to the community. There are numerous blackouts due to interfering the trees with the power transmission lines in hilly and urban areas. Power distribution companies are facing a challenge to monitor the vegetation to avoid blackouts and flash-over threats. Recently, several methods have been developed for vegetation monitoring; however, existing methods are either not accurate or could not provide better disparity map in the textureless region. Moreover, are not able to handle depth discontinuity in stereo thus are not able to find a feasible solution in the smooth areas to compute the disparity map. This study presents a cost-effective framework based on UAV and satellite Stereo images to monitor the trees and vegetation, which provide better disparity. We present a novel approach based on the fusion of the convolutional neural network (CNN) and sparse representation that handled textureless region, depth discontinuity and smooth region to produce better disparity map that further used for threat estimation using height and distance of vegetation/trees near power lines and poles. Extensive experimental evaluation on real time powerline monitoring showed considerable imporvemnt in vegetation threat estimation with accuracy of 90.3% in comparison to graph-cut, dynamic programming, belief propagation, and area-based methods.

Automated Power Lines Vegetation Monitoring Using High-Resolution Satellite Imagery

Vegetation Management is a significant preventive maintenance expense in many power transmission and distribution companies. Traditional Vegetation Management operational practices have proven ineffective and are rapidly becoming obsolete due to the lack of frequent inspection of vegetation and environmental states. The rise of satellite imagery data and machine learning provides an opportunity to close the loop with continuous data-driven vegetation monitoring. This paper proposes an automated framework for monitoring vegetation along power lines using high-resolution satellite imagery and a semi-supervised machine learning algorithm. The proposed satellite-based vegetation monitoring framework aims to reduce the cost and time of power line monitoring by partially replacing ground patrols and helicopter or drone inspection with satellite data analytics. It is implemented and demonstrated for a power distribution system operator (DSO) in the west of Norway. For further assessment, the satellite-based algorithm outcomes are compared with LiDAR survey data collected by helicopters. The results show the potential of the solution for reducing the monitoring costs for electric utilities.

Dynamic Line Rating—An Effective Method to Increase the Safety of Power Lines

Exceeding the electric field’s limit value is not allowed in the vicinity of high-voltage power lines because of both legal and safety aspects. The design parameters of the line must be chosen so that such cases do not occur. However, analysis of several operating power lines in Europe found that the electric field strength in many cases exceeds the legally prescribed limit for the general public. To illustrate this issue and its importance, field measurement and finite element simulation results of the low-frequency electric field are presented for an active 400 kV power line. The purpose of this paper is to offer a new, economical expert system based on dynamic line rating (DLR) that utilizes the potential of real-time power line monitoring methods. The article describes the expert system’s strengths and benefits from both technical and financial points of view, highlighting DLR’s potential for application. With our proposed expert system, it is possible to increase a power line’s safety and security by ensuring that the electric field does not exceed its limit value. In this way, the authors demonstrate that DLR has other potential applications in addition to its capacity-increasing effect in the high voltage grid.

Design of power line safety operation and maintenance monitoring system based on cloud computing

Aiming at the problem of long response time of the traditional power line operation and maintenance monitoring system, a kind of power line safety operation and maintenance monitoring system based on cloud computing is proposed. The secure access platform is used to connect the mobile terminal and the mobile application server, and the data is stored by means of flash external. The signal transmission of the sensor and the analogue-to-digital converter is realised by the signal amplification operation circuit. The user allocation of the user interface is carried out by the HASH algorithm to realise the safety operation and maintenance monitoring of power lines. Compared with the three existing methods, the results show that the maximum response time of the system is 2.79 s, and the time is the shortest, indicating that the system is more suitable for the operation and maintenance monitoring of power lines.

Reference-Free Real-Time Power Line Monitoring Using Distributed Anti-Stokes Raman Thermometry for Smart Power Grids

We report the experimental demonstration of a reference-free Distributed Anti-Stokes Raman Thermometry (DART) scheme for real-time power line monitoring in overhead power transmission (OPGW/OPC) cables. Our work is based on the loop configuration in which only the anti-Stokes intensity is captured and processed to determine the temperature, thereby providing a self-calibrated solution that is tailor-made for a rugged field measurement. Temperature experienced by the optical fiber embedded in the power cable is estimated through a simple heat transfer model and is experimentally validated using a homebuilt DART system with a root mean square (RMS) temperature error of 0.33 °C.