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In this work, we have analysed the different causes of admissions of stone martens (Martes foina), genets (Genetta genetta) and pine martens (Martes martes) in two rehabilitation centres in Castilla y León (Spain) during 32 years (1991–2022). The results indicate that the most important factor for stone martens was road kills with 227 martens (53.4 %) followed by electrocutions with 90 martens (21.2 %). From genets, 94 of them (68.6 %) were due to roadkill and 7 (5.1 %) were due to electrocution. The most important cause of admission for pine martens was road kills with 22 (88 %) and 1 (4 %) due to electrocution. Possible explanations for the observed high mortality of stone martens in electric pylons are discussed. [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]

The energy transition relies on an increasingly massive and pervasive use of renewable energy sources, mainly photovoltaic and wind, characterized by an intrinsic degree of production uncertainty, mostly due to meteorological conditions variability that, even if accurately estimated, can hardly be kept under control. Because of this limit, continuously monitoring the operative status of each renewable energy-based power plant becomes relevant in order to timely face any other uncertainty source such as those related to the plant operation and maintenance (O&M), whose effect may become relevant in terms of the levelized cost of energy. In this frame, the use of robots, which incorporate fully automatic platforms capable of monitoring each plant and also allow effective and efficient process operation, can be considered a feasible solution. This paper carries out a review on the use of robots for the O&M of photovoltaic, wind, hydroelectric, and concentrated solar power, including robot applications for controlling power lines, whose role can in fact be considered a key complementary issue within the energy transition. It is shown that various robotic solutions have so far been proposed both by the academy and by industries and that implementing their use should be considered mandatory for the energy transition scenario. [ABSTRACT FROM AUTHOR]

Illegal killing of protected nongame birds is pervasive and can be demographically relevant. In 2021 and 2022, we evaluated spatial and temporal patterns in illegal killing of birds along 69.7 km of power lines in the Morley Nelson Snake River Birds of Prey National Conservation Area in Idaho, USA, to provide insight into potential drivers behind the activity and key information to manage this threat across the American west. The illegal shooting of 8 species of raptors and corvids we documented was clumped both temporally and spatially, as opposed to being randomly distributed across the year and landscape. We found 72 illegally shot birds, most killed during spring months (March to May), coincident with peak time periods of legal recreational shooting activity, and in places with high levels of recreational shooting. We also found evidence of targeted killing of raptors in the conservation area in areas not associated with recreational shooting. Given the numbers of nesting pairs of some local raptor species, this shooting is likely demographically relevant for some but not all local populations. Likewise, with the prevalence of recreational shooting across the American west, the inference we draw is broadly relevant beyond our Idaho study area. The insight our work provides can enable owners of power lines, law enforcement agencies, and resource managers to coordinate in outreach, regulatory, and law enforcement action to manage a threat that may have widespread impacts for some avian species. [ABSTRACT FROM AUTHOR]

The aerial inspection of electricity infrastructure is gaining high interest due to the rapid advancements in unmanned aerial vehicle (UAV) technology, which has proven to be a cost- and time-effective solution for deploying computer vision techniques. Our objectives are focused on enabling the real-time detection of key power line components and identifying missing caps on insulators. To address the need for real-time detection, we evaluate the latest single-stage object detector, YOLOv8. We propose a fine-tuned model based on YOLOv8's architecture, trained on a custom dataset with three object classes, i.e., towers, insulators, and conductors, resulting in an overall accuracy rate of 83.8% (mAP@0.5). The model was tested on a GeForce RTX 3070 (8 GB), as well as on a CPU, reaching 243 fps and 39 fps for video footage, respectively. We also verify that our model can serve as a baseline for other power line detection models; a defect detection model for insulators was trained using our model's pre-trained weights on an open-source dataset, increasing precision and recall class predictions (F1-score). The model achieved a 99.5% accuracy rate in classifying defective insulators (mAP@0.5). [ABSTRACT FROM AUTHOR]

Electrical power systems represent an immense source of electromagnetic radiation spanning a wide frequency range, encompassing low-frequency, transient states, and high-frequency emissions. The radiation poses a significant self-pollution challenge for electric and electronic devices situated in close proximity to these systems. The present article aims to address this research, and provides a comprehensive review of the electromagnetic effects and pollution at differences frequency's ranges encompassing power systems network. In the context of the low-frequency range, the computation of electric and magnetic fields is conducted using quasi-static principles. To validate the behavior at high frequencies due to power line currents, Schelkunoff's approach is employed. Additionally, transient electromagnetic fields are analyzed using an exact analytical method. The results of computer simulations are compared with previously published and measured data for low-frequency regime and transient overvoltage conditions, verifying their consistency. Additionally, high-frequency results are presented and corroborated through the utilization of a numerical electromagnetic code (NEC-4). These analyses underscore the valuable role of computer-based calculations in examining the electromagnetic environment of power system networks. [ABSTRACT FROM AUTHOR]

The main aspects of the use of automatic monitoring and control systems in order to increase the reliability of power lines are considered. The article highlights modern technologies and techniques that make it possible to quickly identify and prevent possible emergency situations on power transmission lines. The advantages of automated systems compared to traditional monitoring and control methods are discussed, and examples of research and practical applications of such systems are presented. The results obtained can be useful for energy specialists and engineers involved in the design, operation and maintenance of power transmission lines, as well as for developers and manufacturers of automated monitoring and control systems. [ABSTRACT FROM AUTHOR]

This document is a list of references cited in a research article about the abundance and conservation of raptors in Argentina. The references cover various aspects of raptor research, including breeding ecology, habitat relationships, and threats such as electrocution and power line collisions. The document also includes references on the use of anthropogenic nest substrates by Crested Caracaras and the installation of devices in water tanks to prevent drowning of wild animals. This comprehensive list of references can be a valuable resource for library patrons interested in raptor research in Argentina. [Extracted from the article]

The Bonelli's Eagle (Aquila fasciata) is a critically endangered species in Israel, with electrocution on power lines posing a serious threat to its population. Because retrofitting of electricity pylons to prevent mortality is a slow and costly process, it is important to prioritize the pylons in the network for quick and efficient mitigation of eagle mortality. To determine which pylons need to be retrofitted, we applied a three-stage maximum entropy modeling process for identifying the risk factors posed by different environmental variables. The environmental feature with the highest correlation to electrocution is the distance to reservoirs (i.e. many electrocution events occur near water reservoirs). The reservoirs are foraging hotspots for Bonelli's Eagles in Israel's arid environment. Electricity pylons powering the reservoirs' pumping facilities tend to be the highest perches in the vicinity of many of the reservoirs, creating an ecological trap. The strong attraction of reservoirs to eagles may explain the high level of selectivity indicated by the model, suggesting that retrofitting only 3.6% of the pylons in the network would achieve 77% reduction in eagles' electrocution probability. Moreover, insulating pylons according to the model will also likely reduce electrocutions of other avian species, including Eastern Imperial Eagle (Aquila heliaca) and White-tailed Eagle (Haliaeetus albicilla). The modeling process presented here yielded 2 electrocution risk maps, one to facilitate prioritization of mitigation in Israel's existing power network and the second to support planning and designing new infrastructure. The model may help reach conservation goals for the Israeli Bonelli's Eagle and the modeling approach may also be useful in prioritizing pylon retrofitting in other arid landscapes. [ABSTRACT FROM AUTHOR]

Studying the impact of renewable energy sources planned to be connected to the grid, requires the preparation of expert opinions. The task of this opinion is to verify that there are possibilities enabling the connection of the considered source to the network. Each opinion is required to take into account other facilities and those sources which were previously connected to the grid or connection agreement were signed with them. The need to take into account such a large number of sources contributes to potential thermal overloads of high-voltage lines. Sometimes these overloads are insignificant, but in certain situations it turns out that their occurrence may be a reason for refusing to sign connection agreements for new sources. According to network operators, their presence may constitute a threat to the operational security of the grid. The article presents the use of the method of tracking active power flows and the DC method of determining power flows to estimate the impact of these sources on thermal overloading of lines. Using of the IEEE-118 test network, selected nodes were analysed where connecting sources might significantly worsen overloads previously observed or would cause new overloads. The proposed approach will enable potential investors to make proper decisions regarding selection of source connection points. Combining the results obtained by both methods at the same time will allow for the indication of appropriate connection nodes for sources from the point of view of minimising the number of overloaded lines and prospective costs of their uprating. [ABSTRACT FROM AUTHOR]

In the 20th century, there were fewer energy choices. Almost everyone generated their own heating at home, from coal, oil or natural gas, and virtually nobody produced their own electricity. [...]

. It is a very challenging task to correctly use weather data to calculate the active power loss due to corona in overhead power lines. One of the significant reasons for this was the problem of accuracy of determining weather conditions and the use of the forecast for calculating extended overhead lines. In the USSR (Union of Soviet Socialist Republics), based on field tests and experimental data, recommendations were developed for accounting for losses of electric energy due to corona and interference in overhead lines, the average values of which were given in the relevant instruction for the regulation and justification of the norm of electricity consumption for its transmission over electric networks. According to this regulatory document, the specific cost of active power due to corona in overhead lines, averaged by design, was determined depending on weather conditions, which were divided into four groups. In this paper, the effect of active power losses on corona is estimated using various factors on the example of high-voltage overhead lines in the main electrical networks of the Republic of Belarus of various rated voltages. Dependences of active power losses due to corona have been constructed and modeled, taking into account different climatic and weather conditions observed in the country. The determination of metrological parameters was based on the analysis of weather data and patterns of types of weather conditions. In order to improve the accuracy of calculating the specific losses of active power due to corona, all possible weather conditions were considered, as well as their combinations. Afterwards, the data on weather conditions were processed and their types were reduced to one of the four existing types of weather. A method for determining electric power losses due to corona in overhead lines based on the average relative air density and geometric parameters of the predominant section of overhead lines has been proposed and presented. It is shown that the usage of exclusively rated voltage can cause errors in determining both the specific and actual values of active power losses for corona.

The inspection of power transmission and distribution systems is performed visually by foot patrolling and helicopter methods. These methods have various disadvantages such as time-consuming, high operating cost, safety issues, and improper results. To overcome this issue, power utility companies are searching for the alternatives like Unmanned Aerial Systems (UAS) or drones. UAS are safe, cost effective, and requires less time for power transmission line inspection compared to the regular methods. In this manuscript, a decisive flight path planning for UAS to visually inspect a power transmission line and towers is explained. The objective of this paper is to maximize the performance of three functions such as coverage of transmission tower, quality of captured image, and flight time. Second, proposing an automated inspection strategy for UAS to follow the overhead power transmission lines. These objectives are achieved by formulating a cost function, to convert the path planning into a safe operation for UAS. The results of Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are compared to find the best path for UAS. The experimental finding shows that PSO algorithm has higher efficiency and effectiveness compared to the SA algorithm and benefits the UAS with a safe flight path for the inspection of power transmission towers. [ABSTRACT FROM AUTHOR]

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Developing countries are witnessing rapid expansion of electrical infrastructure to meet increasing energy demands, prompting a critical need to assess the potential impact on avian biodiversity. Leveraging open access data, our study conducted a comprehensive assessment to detect electrocution and collision risk hotspots in Kenya while identifying raptor species highly susceptible to these risks. Through an integrated approach that considers morphological and behavioral traits of the species, environmental factors at the site, and technical parameters of the power lines, we developed risk maps and categorized raptors’ susceptibility into high, medium, and low-risk levels. Applying this framework, we identified three raptor species at high risk of electrocution; the long-crested eagle, the augur buzzard, and the steppe eagle. Meru and Kiambu counties emerged as high-risk areas due to substantial overlap between high-risk buffer zones and areas with high raptor species distribution. It is worth noting that this framework only accounts for thirteen raptor species, and there is scope to expand it in the future to include other avian species, such as storks, bustards, pelicans and flamingos, which are also susceptible to electrocution and collision incidents and measures to mitigate electrocution of raptors may not be sufficient for these other group of birds.

With the rapid development of the national economy, power security is very important for the security of the country and people's happiness. Electricity is an important energy source for a country. Even if the power system malfunctions for a short period of time, it would cause incalculable losses to social production and people's lives. Among them, one of the most important reasons for power system faults is the occurrence of power line faults, so diagnosing faulty lines has great research significance. On the basis of analyzing the structure and working principle of the deep learning model convolutional neural network (CNN), this article used the CNN model to diagnose faults in power lines and analyzed the simulation results. It was found that different CNN structures have different fault diagnosis accuracy for power lines. The fewer the number of batches in the network structure and the more the number of training sessions, the higher its fault determination accuracy. In the power line fault diagnosis based on three deep learning algorithms, the CNN has the highest stable fault diagnosis accuracy of 100%; the recursive neural network has the second stable fault diagnosis accuracy of 93.4%; the deep belief network has the lowest stable fault diagnosis accuracy of 91.5%. In the comparison of power line fault diagnosis stability, the accuracy standard deviation of CNN is close to 0, and they are also the most stable in power circuit fault diagnosis. The stability of algorithmic recurrent neural networks is between the two, and the accuracy standard deviation of deep belief networks is 1.84% when trained 12 times. Their fault diagnosis stability is also the worst.

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.

The aerial inspection of electricity infrastructure is gaining high interest due to the rapid advancements in unmanned aerial vehicle (UAV) technology, which has proven to be a cost- and time-effective solution for deploying computer vision techniques. Our objectives are focused on enabling the real-time detection of key power line components and identifying missing caps on insulators. To address the need for real-time detection, we evaluate the latest single-stage object detector, YOLOv8. We propose a fine-tuned model based on YOLOv8’s architecture, trained on a custom dataset with three object classes, i.e., towers, insulators, and conductors, resulting in an overall accuracy rate of 83.8% (mAP@0.5). The model was tested on a GeForce RTX 3070 (8 GB), as well as on a CPU, reaching 243 fps and 39 fps for video footage, respectively. We also verify that our model can serve as a baseline for other power line detection models; a defect detection model for insulators was trained using our model’s pre-trained weights on an open-source dataset, increasing precision and recall class predictions (F1-score). The model achieved a 99.5% accuracy rate in classifying defective insulators (mAP@0.5).

The power sector is one of the most important engineering sectors, with a lot of equipment that needs to be appropriately maintained, often spread over large areas. With the recent advances in deep learning techniques, many applications can be developed that could be used to automate the power line inspection process, replacing previously manual activities. However, in addition to these novel algorithms, this approach requires specialized datasets, collections that have been properly curated and labeled with the help of experts in the field. When it comes to visual inspection processes, these data are mainly images of various types. This paper consists of two main parts. The first one presents information about datasets used in machine learning, especially deep learning. The need to create domain datasets is justified using the example of the collection of data on power infrastructure objects, and the selected repositories of different collections are compared. In addition, selected collections of digital image data are characterized in more detail. The latter part of the review also discusses the use of an original dataset containing 2630 high-resolution labeled images of power line insulators and comments on the potential applications of this collection. [ABSTRACT FROM AUTHOR]

PurposeThe type of dwelling where a child lives is an important factor when considering residential exposure to environmental agents. In this paper, we explore its role when estimating the potential effects of magnetic fields (MF) on leukemia using data from the California Power Line Study (CAPS). In this context, dwelling type could be a risk factor, a proxy for other risk factors, a cause of MF exposure, a confounder, an effect-measure modifier, or some combination.MethodsWe obtained information on type of dwelling at birth on over 2,000 subjects. Using multivariable-adjusted logistic regression, we assessed whether dwelling type was a risk factor for childhood leukemia, which covariates and MF exposures were associated with dwelling type, and whether dwelling type was a potential confounder or an effect-measure modifier in the MF-leukemia relationship under the assumption of no-uncontrolled confounding.ResultsA majority of children lived in single-family homes or duplexes (70%). Dwelling type was associated with race/ethnicity and socioeconomic status but not with childhood leukemia risk, after other adjustments, and did not alter the MF-leukemia relationship upon adjustment as a potential confounder. Stratification revealed potential effect-measure modification by dwelling type on the multiplicative scale.ConclusionDwelling type does not appear to play a significant role in the MF-leukemia relationship in the CAPS dataset as a leukemia risk factor or confounder. Future research should explore the role of dwelling as an effect-measure modifier of the MF-leukemia association.

The association between childhood leukemia and extremely low frequency magnetic fields (ELF-MF) generated by power lines and various electric appliances has been studied extensively during the past 40 years. However, the conditions under which ELF-MF represent a risk factor for leukemia are still unclear. Therefore, we have performed a systematic review and meta-analysis to clarify the relation between ELF-MF from several sources and childhood leukemia. We have systematically searched Medline, Scopus, Cochrane Database of Systematic Review and DARE to identify each article that has examined the relationship between ELF-MF and childhood leukemia. We have performed a global meta-analysis that takes into account the different measures used to assess magnetic field exposure: magnetic flux density measurements (<0.2 µT vs. >0.2 µT), distances between the child's home and power lines (>200 m vs. <200 m) and wire codings (low current configuration vs. high current configuration). Moreover, meta-analyses either based on magnetic flux densities, on proximity to power lines or on wire codings have been performed. The association between electric appliances and childhood leukemia has also been examined. Of the 863 references identified, 38 studies have been included in our systematic review. Our global meta-analysis indicated an association between childhood leukemia and ELF-MF (21 studies, pooled OR=1.26; 95% CI 1.06–1.49), an association mainly explained by the studies conducted before 2000 (earlier studies: pooled OR=1.51; 95% CI 1.26–1.80 vs. later studies: pooled OR=1.04; 95% CI 0.84–1.29). Our meta-analyses based only on magnetic field measurements indicated that the magnetic flux density threshold associated with childhood leukemia is higher than 0.4 µT (12 studies, >0.4 µT: pooled OR=1.37; 95% CI 1.05–1.80; acute lymphoblastic leukemia alone: seven studies, >0.4 µT: pooled OR=1.88; 95% CI 1.31–2.70). Lower magnetic fields were not associated with leukemia (12 studies, 0.1–0.2 µT: pooled OR=1.04; 95% CI 0.88–1.24; 0.2–0.4 µT: pooled OR=1.07; 95% CI 0.87–1.30). Our meta-analyses based only on distances (five studies) showed that the pooled ORs for living within 50 m and 200 m of power lines were 1.11 (95% CI 0.81–1.52) and 0.98 (95% CI 0.85–1.12), respectively. The pooled OR for living within 50 m of power lines and acute lymphoblastic leukemia analyzed separately was 1.44 (95% CI 0.72–2.88). Our meta-analyses based only on wire codings (five studies) indicated that the pooled OR for the very high current configuration (VHCC) was 1.23 (95% CI 0.72–2.10). Finally, the risk of childhood leukemia was increased after exposure to electric blankets (four studies, pooled OR=2.75; 95% CI 1.71–4.42) and, to a lesser extent, electric clocks (four studies, pooled OR=1.27; 95% CI 1.01–1.60). Our results suggest that ELF-MF higher than 0.4 µT can increase the risk of developing leukemia in children, probably acute lymphoblastic leukemia. Prolonged exposure to electric appliances that generate magnetic fields higher than 0.4 µT like electric blankets is associated with a greater risk of childhood leukemia. [ABSTRACT FROM AUTHOR]

In this research, an image defogging algorithm is proposed for the electricity transmission line monitoring system in the smart city. The electricity transmission line image is typically situated in the top part of the image which is rather thin in size. Because the electricity transmission line is situated outside, there is frequently a sizable amount of sky in the backdrop. Firstly, an optimized quadtree segmentation method for calculating global atmospheric light is proposed, which gives higher weight to the upper part of the image with the sky region. This prevents interference from bright objects on the ground and guarantees that the global atmospheric light is computed in the top section of the image with the sky region. Secondly, a method of transmission calculation based on dark pixels is introduced. Finally, a detail sharpening post-processing based on visibility level and air light level is introduced to enhance the detail level of electricity transmission lines in the defogging image. Experimental results indicate that the algorithm performs well in enhancing the image details, preventing image distortion and avoiding image oversaturation. [ABSTRACT FROM AUTHOR]

Biodiversity linked to traditionally managed semi‐natural grasslands is declining, despite conservation efforts. At the same time, the area of novel grassland habitats along linear infrastructure, such as road verges and power line corridors, is increasing and in some regions surpass the area of semi‐natural grasslands. An open question is to what extent these novel grasslands can complement or even replace traditionally managed grasslands as habitat for grassland species.We compared the alpha (species richness) and beta (abundance‐based dissimilarities) diversity of plants, bumblebees and butterflies among semi‐natural pastures, verges of small and big roads, power line corridors and uncultivated field borders nested within 32 landscapes (2 × 2 km squares). Landscapes had either high or low road density and were with or without power line corridors. Across landscapes there was also a gradient in the area of semi‐natural pastures.Alpha diversity of all three species groups was as high in power line corridors and verges of small roads as in semi‐natural pastures, regardless of landscape composition. Although all habitat types shared a large proportion of species, community composition differed among habitats for all three species groups. The beta diversity of plants and butterflies was driven primarily by the replacement of species (turnover), while the beta diversity of bumblebees was driven by a rarer occurrence of certain species in road verges (nestedness). This means that linear infrastructure habitats cannot fully replace the role of semi‐natural grasslands for plant and pollinator diversity.The area of road verges, power line corridors and semi‐natural pastures in the landscape influenced community composition of plants and butterflies, but not the similarity in community composition among habitats within landscapes.Policy implications. Although novel grasslands along linear infrastructures have high numbers of grassland species, they only support a part of the biodiversity found in traditionally managed semi‐natural grasslands. Therefore, protecting and restoring semi‐natural grasslands should continue to be a priority for the conservation of grassland biodiversity. However, especially in landscapes where the area of semi‐natural grasslands is low, road verges and power line corridors can be important habitats for a number of grassland plants and insects and should be managed to promote biodiversity. [ABSTRACT FROM AUTHOR]

The combined effects of environmental factors such as high winds and melting ice can cause transmission line conductors to vibrate at high amplitudes, resulting in damaged pole structures, cracked insulating strands, and short circuits. The manual installation of electrical spacers between the two power line conductors is currently the only way to prevent this, but due to the high-voltage environment, this operation is extremely dangerous for a human worker. As a solution to automate this operation, the autonomous installation of electrical spacers using a robotic manipulator is proposed. For this purpose, a design of a special end effector for the robotic installation of electrical spacers is proposed. The end effector prototype was produced and tested under laboratory conditions and then used for the autonomous installation of spacers on power lines. Its localization with respect to the power lines is based on measurements of the magnetic field generated by the alternating currents flowing through the power lines. To verify the feasibility of the proposed solution under laboratory conditions, the proposed end effector equipped with magnetometers was developed and mounted on a 6-axis Schunk LWA 4p robotic arm. The implemented autonomous installation sequence was successfully verified using a robot and a laboratory mock-up of power lines. [ABSTRACT FROM AUTHOR]

Brief legal analysis of the Sentence of the Superior Court of Justice of Castilla-La Mancha on the occasion of the Contentious-Administrative Appeal 554/2020, filed against Agreement of the Government Council of the Junta de Comunidades de Castilla-La Mancha in disciplinary proceedings SA11/2020 (45CN180209). Disregard by the Superior Court of Justice of the requirements of the Law 26/2007 on Environmental Responsibility for considering it generic and for not appreciating guilt for being the concrete electrical pole that caused the electrocution of an Iberian Imperial Eagle outside the Protection Zones defined in article 4 of Royal Decree 1432/2008. Inadequate judgment of reproach of the sanctioned conduct. [ABSTRACT FROM AUTHOR]

A challenge for inspecting transmission power lines with Unmanned Aerial Vehicles (UAVs) is to precisely determine their position and orientation, considering that the geo-location of these elements via GPS often needs to be more consistent. Therefore, a viable alternative is to use visual information from cameras attached to the central part of the UAV, enabling a control technique that allows the lines to be positioned at the center of the image. Therefore, this work proposes a PID (proportional–integral–derivative) controller tuned through interval type-2 fuzzy logic (IT2_PID) for the transmission line follower problem. The PID gains are selected online as the position and orientation errors and their respective derivatives change. The methodology was built in Python with the Robot Operating System (ROS) interface. The key point of the proposed methodology is its easy reproducibility, since the designed control loop does not require the mathematical model of the UAV. The tests were performed using the Gazebo simulator. The outcomes demonstrated that the proposed type-2 fuzzy variant displayed lower error values for both stabilization tests (keeping the UAV centered and oriented with the lines) and the following step in which the trajectory is time-variant, compared to the analogous T1_PID control and a classical PID controller tuned by the Zigler–Nichols method. [ABSTRACT FROM AUTHOR]

This work focuses on the automatic identification of forest fire risk areas along high-voltage power lines through the development of a tool and its validation on a real forest area. The tool allows one to automate the whole process, which includes the classification of the point cloud, the computation of the catenary of the wires using different calculation methods, the estimation of the vegetation growth and the identification of the risk areas. To this end, a coarse-to-fine approach is proposed, so that a preliminary analysis is performed with public airborne LiDAR data, and then a more detailed inspection is provided with drone LiDAR data over those areas classified as high risk. The tool and the methodology developed were validated along a high-voltage power line of 53 km in a real forest area. The results show that although the preliminary analysis based on public airborne LiDAR data is more conservative, it is very useful for selecting those areas of higher risk for further analysis with drone LiDAR data. [ABSTRACT FROM AUTHOR]

The inspection and maintenance of electrical power lines (PL) via unmanned aerial vehicles (UAV) require fast and accurate PL detection to ensure smooth and secure electrical operations. However, the detection of PLs from aerial images is a highly challenging task due to the thin nature of PLs and the inherent noisy image backgrounds. Traditional line and edge detection methods do not detect the PLs accurately due to the cluttered backgrounds while the more recent deep learning (DL) CNNs are also not feasible for efficient PL detection due to the coarse bounding boxes and the computationally expensive pixel-based segmentations. Hence, in this study we propose PLPose, a novel framework for detecting the PLs via key points-based pose estimation technique and adapt the MobileNetV3 CNN for this task (kMobileNetV3), by adding a simple key point detection head to predict the PL key points. We also introduce a novel data-centric architecture (kMobileNetV3 + UDP), by adding the unbiased data processing (UDP) module to our kMobileNetV3, for faster and more accurate key point-based PL detection along with novel methods for data annotations and performance evaluation. Evaluations of PLPose on three benchmark PL datasets (PLDM, PLDU and the Mendeley Powerline Dataset) reveal that our proposed framework outperforms the state-of-the-art top-down pose estimation networks (HRNet-w32, HRNet-w32 + UDP and Resnet-50 Simple Baselines) in processing speed (∼29 FPS) and model size (5.23 M) for PL detection. Thus, the comprehensive experimental results demonstrate the effectiveness of our proposed framework. Our code is available from Github (https://www.github.com/rubeea/pl_mmpose).

The incidence of wildfires caused by tree contact with high-voltage power lines has become an increasingly pressing issue in the United States. To prevent such incidents, local safety councils have established minimum clearance regulations between trees and power lines. While most studies have focused on the tree encroachment around power lines during normal weather conditions, recent catastrophic fires have been caused by strong winds. To address this gap in knowledge, we investigated the critical wind speed that heightens the risk of wildfires by calculating the distance between trees and wires. To conduct this study, we used airborne LiDAR data collected from Sonoma County in northern California and analyzed the behavior of a sample tree having a height of 19.2 m under wind loads. Our analysis showed that the main factor determining tree deflection is the ratio of the tree height to the trunk diameter. We also found that, although the probability of fire ignition is typically low under normal conditions, it is likely to increase at a wind speed of approximately 40.3 m/s. In conclusion, this research demonstrates the utility of point cloud data in identifying potentially dangerous trees and reducing the risk of fires. [ABSTRACT FROM AUTHOR]

Electricity has become an indispensable source of energy, and power lines play a crucial role in the functioning of modern societies. It is essential to inspect power lines promptly and precisely in order to ensure the safe and secure delivery of electricity. In steep and mountainous terrain, traditional surveying methods cannot inspect power lines precisely due to their nature. Remote sensing platforms, such as satellite and aerial images, thermal images, and light detection and ranging (LiDAR) points, were utilised for the detection and inspection of power lines. Nevertheless, with the advancements of remote sensing technologies, in recent years, LiDAR surveying has been favoured for power line corridor (PLC) inspection due to active and weather-independent nature of laser scanning. Laser ranging data and the precise location of the LiDAR can be used to generate a three-dimensional (3D) image of the PLC. The resulting 3D point cloud enables accurate extraction of power lines and measurement of their distances from the forest below. In the literature, there have been many proposals for power line extraction and reconstruction for PLC modelling. This article examines the pros and cons of each domain method, providing researchers involved in three-dimensional modelling of power lines using innovative LiDAR scanning systems with useful guidelines. To achieve these objectives, research papers were analysed, focusing primarily on geoscience-related journals and conferences for the extraction and reconstruction of power lines. There has been a growing interest in examining the extraction and reconstruction of power line spans with single and multi-conductor configurations using different image and point-based techniques. Our study provides a comprehensive overview of the methodologies offered by various approaches using laser scanning data from the perspective of power line extraction applications, as well as to discuss the benefits and drawbacks of each approach. The comparison revealed that, despite the tremendous potential of aerial and mobile laser scanning systems, human intervention and post-processing actions are still required to achieve the desired results. In addition, the majority of the methods have been evaluated on the small datasets, and very few methods have been focused on multi-conductor extraction and reconstruction for power lines modelling. These barriers hinder the automated extraction and reconstruction of power line using LiDAR data and point to unexplored areas for further research and serve as useful guidelines for future research directions. Several promising directions for future LiDAR experiments using deep learning methods are outlined in the hope that they will pave the way for applications of PLC modelling and assessment at a finer scale and on a larger scale. [ABSTRACT FROM AUTHOR]

The existing methods for calculating electromagnetic scattering can be used to obtain the RCS of power lines. However, these methods do not take advantage of the periodicity of power lines. We propose a fast electromagnetic scattering calculation method combining the integral equation discontinuous Galerkin (IEDG) method and the characteristic modes-Sherman–Morrison–Woodbury algorithm (CM-SMWA) exploiting the power lines with stranded structure. We adopt the IEDG to discretize the electric field integral equation (EFIE) so that the EFIE can deal with non-conformal grids and significantly increase the flexibility of the CM-SMWA. Combing with the periodic property of power lines, the modeling and grid generation shall be carried out within one cycle (stranding) of the power line, and the grids of the rest cycle of the power line can be spliced by translating the grid of the divided sections. The advantage of the proposed method lies in that only the CM of one segment needs to be calculated, and the result can be applied to other segments to avoid repeated calculation of the CMs. The simulation results of the RCS of power lines show that the calculation time of our method is cut down by 50% as compared to the conventional CM-SMWA. [ABSTRACT FROM AUTHOR]

Power and communication line corridors are usually mixed with urban trees, and this mixing can be the source of multiple issues like fires and communication failures. Nevertheless, urban trees are a valuable resource to the city as they dissipate heat island effects, reduce air pollution and increase general health perception. This work proposes a deep learning approach to detect trees entangled to power and communication lines using street-level imagery and perform quick quantitative and qualitative analyses based on the Grad-CAM++ method. Testing the method was performed using 1001 images from urban trees from the cities of São Paulo and Porto Alegre (both in Brazil). We found an overall accuracy of 74.6% (73.6% for São Paulo and 75.6% for Porto Alegre), suggesting that the methodology could be suitable in the future for city management to avoid risks of accidents due to contact between trees and electrical wiring. This text describes the method, a new data set of urban images, the experimental setup design and tests, and some possible future improvements. [ABSTRACT FROM AUTHOR]

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Epidemiological studies indicate that electromagnetic fields (EMF) are associated with cancer in humans. Exposure to mobile phone specific high frequency fields (HF-EMF) may lead to increased glioma risks, while low frequency radiation (LF-EMF) is associated with childhood leukemia. We studied the impact of HF-EMF (1950 MHz, UMTS signal) on DNA stability in an astrocytoma cell line (1321N1), and the effect of LF-EMF (50 Hz) in human derived lymphoma (Jurkat) cells. To find out if these fields affect chemically induced DNA damage, co-exposure experiments were performed. The cells were exposed to HF-EMF or LF-EMF and treated simultaneously and sequentially with mutagens. The compounds cause DNA damage via different molecular mechanisms, i.e. pyrimidine dimers which are characteristic for UV light (4-nitroquinoline 1-oxide, 4NQO), bulky base adducts (benzo[ a ]pyrene diolepoxide, BPDE), DNA-DNA and DNA-protein cross links and oxidative damage (NiCl 2 , CrO 3). DNA damage was measured in single cell gel electrophoresis (comet) assays. We found a moderate reduction of basal and 4NQO-induced DNA damage in the astrocytoma line, but no significant alterations of chemically induced DNA migration by the HF and LF fields under all other experimental series. The biological consequences of the moderate reduction remain unclear, but our findings indicate that acute mobile phone and power line specific EMF exposures do not enhance genotoxic effects caused by occupationally relevant chemical exposures. • Mobile phone-specific (HF) and low-frequency (LF) electromagnetic fields (EMF) may cause cancer. • We investigated the impact of these fields on DNA damage in human-derived cells. • The cells were exposed to the EMF in combination with occupationally related carcinogens. • We found no evidence for synergistic effects. • In neural cells, we observed a significant reduction of DNA damage after exposure to the HF field. [ABSTRACT FROM AUTHOR]

AimsStudies of environmental exposures and childhood leukemia studies do not usually account for residential mobility. Yet, in addition to being a potential risk factor, mobility can induce selection bias, confounding, or measurement error in such studies. Using data collected for California Powerline Study (CAPS), we attempt to disentangle the effect of mobility.MethodsWe analyzed data from a population-based case-control study of childhood leukemia using cases who were born in California and diagnosed between 1988 and 2008 and birth certificate controls. We used stratified logistic regression, case-only analysis, and propensity-score adjustments to assess predictors of residential mobility between birth and diagnosis, and account for potential confounding due to residential mobility.ResultsChildren who moved tended to be older, lived in housing other than single-family homes, had younger mothers and fewer siblings, and were of lower socioeconomic status. Odds ratios for leukemia among non-movers living

We studied the movements of Common Kestrels Falco tinnunculus in central Italy by GPS-tracking 10 individuals between 2019 and 2021. Our aim was to investigate the extent of movements during the breeding and non-breeding seasons. In the breeding season the mean home-range size increased from incubation (1.11 km2) to the chick-rearing period (3.35 km2), and the average home-range for the entire study period was 3.68 km2. In winter, all tagged individuals remained within a few hundred meters of their nesting area, revealing for the first time a non-migratory behaviour for the species. In conclusion, our study provides novel data on the movement ecology of Kestrels during both the breeding and non-breeding seasons, and documented the resident behaviour of Kestrels in central Italy. [ABSTRACT FROM AUTHOR]

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Yearly electricity production has increased steadily worlwide in recent decades and the associated overhead power lines are widespread and occur across urban and natural habitats, and often in remote areas where there is little other anthropogenic influence. Here we assessed the effects of overhead power lines on the density of ground‐nesting birds in the Icelandic lowlands, which host several populations of international importance. The combined breeding density of the eight study species increased significantly from ~ 112 birds/km2 close (< 50 m) to the power lines to ~ 177 birds/km2 away (450–500 m) from the power lines, with two of these species (Eurasian Whimbrel Numenius phaeopus and Common Redshank Tringa totanus) increasing significantly with distance from power lines and six species (European Golden Plover Pluvialis apricaria, Common Snipe Gallinago gallinago, Meadow Pipit Anthus pratensis, Black‐tailed Godwit Limosa limosa, Dunlin Calidris alpina and Redwing Turdus iliacus) showing no changes. These findings suggest that power lines can influence the breeding density of ground‐nesting bird species in their vicinity and that accounting for such effects when planning future infrastructure will be imperative. [ABSTRACT FROM AUTHOR]

The power sector is one of the most important engineering sectors, with a lot of equipment that needs to be appropriately maintained, often spread over large areas. With the recent advances in deep learning techniques, many applications can be developed that could be used to automate the power line inspection process, replacing previously manual activities. However, in addition to these novel algorithms, this approach requires specialized datasets, collections that have been properly curated and labeled with the help of experts in the field. When it comes to visual inspection processes, these data are mainly images of various types. This paper consists of two main parts. The first one presents information about datasets used in machine learning, especially deep learning. The need to create domain datasets is justified using the example of the collection of data on power infrastructure objects, and the selected repositories of different collections are compared. In addition, selected collections of digital image data are characterized in more detail. The latter part of the review also discusses the use of an original dataset containing 2630 high-resolution labeled images of power line insulators and comments on the potential applications of this collection.

The efficiency of reducing switching overvoltages during reclosing of long high-voltage transmission lines is achieved by intelligent automatic reclosing due to precise control of a circuit-breaker switching instant. The required accuracy of the intelligent automatic reclosing is provided by adaptive control algorithms that take a number of technologically important features of the circuit breaker closing process into account. First, as a rule, the moment of closing the electrical circuit does not coincide with the moment of closing the contacts of the switch due to electrical breakdown of the intercontact gap when the contacts approach each other. Second, the moment of closing the contacts itself deviates from the calculated one due to the spread in the time of the circuit breaker drive action. Third, the operating time of the circuit breaker contact system changes under the influence of environmental conditions and during operation. This paper presents the basics of taking into account these features of the electromechanical characteristics of high-voltage circuit breakers in precision control algorithms. It is shown that the effectiveness of intelligent automatic reclosing is ensured by adaptive-control algorithms that take into account the effect of ambient temperature, drive energy stored, supply voltage of control circuits, breaker idle time, operational wear of the circuit breaker, and the dynamics of changes in the electric strength of the intercontact gap during the cycle of switching. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of the mechanical dynamical response of the transmission overhead line with installed interphase spacers in demanding winter conditions in the presence of ice accretion and ice-shedding on conductors. While the electrical characteristics of the transmission line equipment are determined by the voltage level, the mechanical characteristics require a special analysis. The analysis is performed as a numerical experiment using the method of the absolute nodal coordinates on a typical example of the transmission line. The numerical results give an insight into the forms of the dynamic response of the transmission-line conductors and interphase spacers and determine the mechanical characteristics of the interphase spacers and thus an optimal dimensioning of the transmission line equipment. [ABSTRACT FROM AUTHOR]

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

The radar cross-section (RCS) of power lines has an important significance for detection of the power lines. The method of moments (MoM) can calculate the RCS of power lines. However, the efficiency of the MoM is limited by the time-consuming computing process, as well as the expensive storage overhead. In order to enhance the efficiency and reduce the storage of the RCS calculation of power lines, we propose an RCS calculation method that combines the characteristic mode (CM) with a Sherman–Morrison–Woodbury formula-based algorithm (SMWA), which is referred to as a CM-SMWA. CMs are used as the basis functions for reducing the dimension of the MoM impedance matrix, and the SMWA is applied to directly solve the CMs-reduced matrix equation, which can reduce the computational time and storage. The numerical results demonstrate that the proposed method can obtain the RCS of power lines, with different incident angles and different polarizations, at a higher efficiency. At 35 GHz, compared with the conventional MoM, for a typical LGJ50-8 power line with a length of 0.276 m, the computation time is reduced by 62.4% and the memory occupation is reduced by 96.4%. [ABSTRACT FROM AUTHOR]

THE PURPOSE. paper show digital system for monitoring of single-phase earth faults on overhead power lines of 10 kV voltage in distribution power grids with an isolated neutral mode. METHODS. Digital system analyze frequency and amplitude of higher harmonics of voltage and current. The digital monitoring system includes the features of technical solutions for the selection of higher harmonics. RESULTS. Simultaneously, the paper shows the layout solutions for connecting a monitoring system to the control circuits on the10 kV side of 110/10 kV voltage substation. Software implemented for the far monitoring of the power grids and can detect of various types of single-phase earth faults. CONCLUSION. Software base on the mathematical models, as well as taking into account the results of a series of full-scale experiments of modeling various types of single-phase earth faults in the distribution power grids. The main functionalities the developed software presented for the description of solutions. Paper show further directions of research of the diagnostics and monitoring of single-phase earth faults in distribution power grids with overhead power lines based on the analysis of higher harmonic components.

Norway aims to transform into a low-emission nation by 2050. However, the energy transition comes with challenges, as expanding renewable energy infrastructure requires significant land areas, which raises concerns about associated environmental impacts, particularly on species richness. The first system-wide life cycle assessment (LCA) approach that integrates multiple biodiversity models is presented, evaluating the current effects of renewable energy production and transmission in Norway. The impacts of Norwegian hydropower and onshore wind power plants are quantified, which contribute 98% of Norway's electricity generation. Additionally, the effects of power lines are included to assess the spatial origins of the biodiversity impacts derived from both energy production and transmission relative to where electricity was transmitted to and consumed in Norway. The analyses show that hydropower exerts the most substantial impact on species richness and that the electric grid affects mostly mammal richness due to habitat conversion and fragmentation. As energy production primarily originates in western-central and northern Norway, regions with low electricity generation, such as south-east Norway, import a substantial share of the biodiversity impacts for their energy consumption. However, integrating an international dimension is crucial, as multiple European countries exchange energy with Norway, and thus also contribute to the pressure on local species richness. The results present the current operational impacts on biodiversity caused by Norway's electricity system. Since Norway intends to increase its renewable energy production capacity in the coming decades, this approach can serve as an instrument for developing scenarios to assess the future construction of hydropower, onshore wind power, and power lines. It can help shape Norway's energy future, ensuring a holistic approach that integrates impacts on species diversity and promotes an environmentally friendly energy transition. While focused on Norway, the methodology is adaptable to other regions if the required data is available. • First comparable LCIA approach quantifying biodiversity impacts of energy systems. • System-wide assessment of Norwegian hydropower, wind power, and power lines. • Hydropower has the largest overall biodiversity impact in Norway. • Electricity transmission severely contributes to habitat loss and fragmentation. • Models adaptable for other regions with relevant input data. [ABSTRACT FROM AUTHOR]

There is an increasing expectation for companies to revise their activities to minimize impacts on biodiversity, as well as undertake compensatory and proactive actions to restore biodiversity. To contribute towards global biodiversity goals, such actions need to be both ambitious and effective. Yet, implementation of mitigation actions to avoid impact is often poor or omitted, and actions to minimize and restore can be of varying effectiveness. The financial costs of actions are often seen as a barrier. Here we argue that mitigation costs, both costs incurred or avoided, are often overlooked or over-simplified when designing mitigation strategies. This includes limited quantification of management costs, the costs of potential impacts, assumed counterfactuals (i.e., what would happen without mitigation?), the distribution of costs between stakeholders, and the value gained by enhancing biodiversity. A more detailed and nuanced understanding of the economic costs and benefits of mitigation actions can both help prioritize resources more efficiently and reveal the true cost-effectiveness of mitigation at the project and portfolio levels, particularly for actions that avoid damage to biodiversity. This could improve the implementation of the mitigation hierarchy, as project developers or decision makers have a better understanding of the actual costs and benefits of different potential mitigation strategies. We exemplify this thinking with a detailed case study that determines the costs and effectiveness of a range of actions to reduce bird collisions on power lines. • Understanding the costs of actions is important for efficient conservation. • Yet, costs can often be neglected during mitigation hierarchy application. • A detailed, nuanced assessment of costs influences cost-effectiveness estimates. • We demonstrate this with a case study of power line impacts on bird species. • We discuss opportunities and implications for mitigation decision making. [ABSTRACT FROM AUTHOR]

• We assessed anthropogenic impacts on territorial species using individual based models. • Impacts increased when exposure to anthropogenic mortality affected multiple territories. • Impacts increased when mortality affected both territorial and non-territorial individuals. • Mitigation actions should account for territorial and non-territorial birds. • Our IBM framework can inform strategies to mitigate impacts of anthropogenic mortality. Wildlife anthropogenic mortality is increasing worldwide, yet there is limited understanding regarding its population-level impacts. Territorial species stand out in this context, as they possess distinctive characteristics that are often overlooked but may significantly affect their vulnerability. In particular, population impacts may depend on the level and spatial distribution of additional mortality risk across territories, and on the extent to which exposure to increased mortality varies across life stages (i.e., territorial and non-territorial individuals). In this study, we developed an Individual-Based Model (IBM) to explore these issues, using the Bonelli's eagle (Aquila fasciata) and electrocution in powerline pylons as a model system. We used declines in annual population growth rates as a proxy for negative impacts, and conducted simulations to estimate the relative impacts of different levels of mortality risk, the spatial pattern of such risks, and the risk exposure of different life stages. Population-level impacts greatly increased with the mortality risks simulated, and they were lower when exposure to mortality risks was concentrated versus spread across territories. Impacts were highest when both territorial and non-territorial individuals were exposed to anthropogenic mortality risks, and they were higher when such exposure only affected non-territorial versus territorial individuals. Our results underscore that each breeding territory should be considered as a unit, where all existing pylons should be intervened whenever mitigation actions are put in place. Results also highlight the importance of considering both the territorial and non-territorial fractions of the population to prevent and mitigate the impacts of increased mortality. More generally, our study illustrates the value of IBM frameworks such as ours to explore population-level impacts resulting from anthropogenic mortality in territorial species, and to inform the development of conservation strategies to mitigate such impacts. [ABSTRACT FROM AUTHOR]

The combined effects of environmental factors such as high winds and melting ice can cause transmission line conductors to vibrate at high amplitudes, resulting in damaged pole structures, cracked insulating strands, and short circuits. The manual installation of electrical spacers between the two power line conductors is currently the only way to prevent this, but due to the high-voltage environment, this operation is extremely dangerous for a human worker. As a solution to automate this operation, the autonomous installation of electrical spacers using a robotic manipulator is proposed. For this purpose, a design of a special end effector for the robotic installation of electrical spacers is proposed. The end effector prototype was produced and tested under laboratory conditions and then used for the autonomous installation of spacers on power lines. Its localization with respect to the power lines is based on measurements of the magnetic field generated by the alternating currents flowing through the power lines. To verify the feasibility of the proposed solution under laboratory conditions, the proposed end effector equipped with magnetometers was developed and mounted on a 6-axis Schunk LWA 4p robotic arm. The implemented autonomous installation sequence was successfully verified using a robot and a laboratory mock-up of power lines.

A challenge for inspecting transmission power lines with Unmanned Aerial Vehicles (UAVs) is to precisely determine their position and orientation, considering that the geo-location of these elements via GPS often needs to be more consistent. Therefore, a viable alternative is to use visual information from cameras attached to the central part of the UAV, enabling a control technique that allows the lines to be positioned at the center of the image. Therefore, this work proposes a PID (proportional–integral–derivative) controller tuned through interval type-2 fuzzy logic (IT2_PID) for the transmission line follower problem. The PID gains are selected online as the position and orientation errors and their respective derivatives change. The methodology was built in Python with the Robot Operating System (ROS) interface. The key point of the proposed methodology is its easy reproducibility, since the designed control loop does not require the mathematical model of the UAV. The tests were performed using the Gazebo simulator. The outcomes demonstrated that the proposed type-2 fuzzy variant displayed lower error values for both stabilization tests (keeping the UAV centered and oriented with the lines) and the following step in which the trajectory is time-variant, compared to the analogous T1_PID control and a classical PID controller tuned by the Zigler–Nichols method.

In frigid winter, the ice and snow built‐up on high‐voltage overhead power lines may seriously risk the reliability of electric power transmission and telecommunication systems. Green technologies for power line deicing that can effectively remove the accumulated ice or snow on the cables in a gentle way are highly desired but technically challenging due to the complex cable surfaces. Herein, this work reports a scalable solar‐thermal icephobic nanocoating compatible with both flat and complex curved surfaces. The spray‐coated nanocoating comprises a titanium nitride nanoparticle layer as a low‐emissivity photo‐thermal medium and dual‐scale silica particles as a water‐repellent layer even at low temperatures. Enabled by the collective effects of high‐efficiency solar‐thermal conversion and temperature‐insensitive superhydrophobicity, the nanocoating realizes effective deicing/defrosting on power lines at frigid temperatures down to −15 °C. The versatility of this coating and its compatibility with mass‐production processes render passive solar‐driven deicing technologies promising for practical applications on most outdoor exposed surfaces. [ABSTRACT FROM AUTHOR]