Principle of functioning of smart solution to clean high power lines in cold climate.

Dependency of human activities on electricity supply goes from emergency services to comfort aspects. Reliability of electricity distribution systems is a complex problem to tackle, especially when the systems are located at cold climate regions, dealing with ice accretion on the elements of the electrical systems and its consequences become a priority to be included in maintenance maneuvers in order to guaranty the energy distribution. De-icing methods must demonstrate their effectiveness in removing ice accreted on ground wires and conductors under severe environment conditions. Therefore, these methods are restricted by specific mechanical, electrical and thermal constrains related with the power line operation. Mechanical stresses imposed on the lines by stretching and torsion caused by the ice accreted on the system elements, the weight and action of the de-icing mechanism or wind effects on the structure determine the dynamics restrictions must be considered during installation as well as operation of new deicing mechanisms. Measures to insulate the de-icing mechanisms from electrical and electromagnetic perturbations are needed in order to overcome the electrical restrictions. Risk of damage or affected performance of de-icing mechanisms due to thermal shock during releasing of the high current pulse of lightning through the surface of the conductors, towers or other elements also imposes new set of constrains on the de-icing mechanism. Expansion of electrical system on remote location, with severe winter conditions along with the changes introduced by the climate changes, put extra interests on the technology development of mechanisms to prevent or remove ice from long lines with single or bundled conductors. Research has been carried out including large-scale technologies testing to address this problem. Mechanisms based on thermal effects, shock waves, cutting, or others have been already proposed. In this paper a comprehensive discussion of the existing methods and the comparison with a new proposed mechanism is presented. So, a new functioning principle of percussion will be presented, analyzed and discussed leading to new scenarios of technology development. This method represents a valid alternative that require less energy than the energy is used to melt the ice on the power lines. The implementation of this mechanism is also possible actually a design of the principle of functioning produced with support of external sources

An advanced unmanned aerial vehicle (UAV) approach via learning-based control for overhead power line monitoring: a comprehensive review

Detection and prevention of faults in overhead electric lines is critical for the reliability and availability of electricity supply. The disadvantages of conventional methods range from cumbersome installations to costly maintenance and from lack of adaptability to hazards for human operators. Thus, transmission inspections based on unmanned aerial vehicles (UAV) have been attracting the attention of researchers since their inception. This article provides a comprehensive review for the development of UAV technologies in the overhead electric power lines patrol process for monitoring and identifying faults, explores its advantages, and realizes the potential of the aforementioned method and how it can be exploited to avoid obstacles, especially when compared with the state-of-the-art mechanical methods. The review focuses on the development of advanced Learning Control strategies for higher manoeuvrability of the quadrotor. It also explores suitable recharging strategies and motor control for improved mission autonomy

Icing Effects on Power Lines and Anti-icing and De-icing Methods

Icing on power lines may lead to compromise safety and reliability of electric supply network. Prolong icing can lead to power breakdown and collapse of towers. Since power transmission lines are mostly overhead and could face the direct impact of icing, and it is one of the main challenges faced by power distribution companies in cold regions. When the ice accretion crosses the safety limit then deicing action can be carried out. We can find number of deicing methods that are used in different parts of the world. However, all of these deicing techniques have their own advantages and disadvantages on implementation. It is one of the most difficult as well as dangerous process to perform deicing on power lines. If a fault is detected and that has been occurred due to icing or during routine maintenance, extra care must be taken in order to ensure safety of the personals when performing de-icing of lines. However, as technology evolved, new ways and techniques are adopted with the help of sensors that give quick feedback to control room in the national grid via wireless communication network for real time action. In the thesis we have discussed atmospheric icing impacts on power lines in the cold regions across the world. A literature review has been done for anti-icing and deicing methods that are currently adopted in the power distribution network. Methods that are used against ice buildups have also been analyzed. This work also shows the impacts of icing and deicing techniques presently adopted, and also throws light on their pros and cons during maintenance operations. It provides an overview of the evolving technology trends that are practiced to ensure the availability of existing power transmission system in cold climate regions

Développement et évaluation d'une stratégie d'atterrissage pour drones semi-autonome sur lignes électriques dans différentes conditions de vent

Au cours des dernières années, le recours aux drones pour l’inspection des lignes électriques à haute tension s’est répandu en raison de leur efficacité, de leur rentabilité et de leur ca- pacité à atteindre des zones autrement inaccessibles. Cependant, faire atterrir en toute sécurité ces drones sur les lignes électriques, notamment dans des conditions venteuses, constitue un défi majeur. Cette recherche présente un modèle de contrôle semi-autonome pour permettre l’atterrissage sur une ligne électrique à l’aide de la plateforme NADILE (un drone conçu spécifiquement pour l’inspection des lignes électriques) et évalue le fonc- tionnement dans différentes conditions de vent. L’analyse de la probabilité de réussite de l’atterrissage en fonction de l’état initial du drone a été effectuée à l’aide de la méthode de Monte Carlo. Les performances du système ont été évaluées pour deux stratégies d’atter- rissage différentes, divers paramètres de contrôle, et quatre niveaux de vent. Les résultats ont montré qu’une stratégie d’atterrissage en deux étapes donne de meilleures chances de réussite de l’atterrissage et fournissent des indications précieuses sur les paramètres de contrôle optimaux et le niveau maximal de vent pour lequel le système est fiable. Une dé- monstration expérimentale de l’atterrissage autonome du système sur une ligne électrique a également été réalisée

Design knowledge for deep-learning-enabled image-based decision support systems — evidence from power line maintenance decision-making [in press]

With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. This paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements from literature and the application field are derived. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact\u27s capability to capture selected faults (regarding insulators and safety pins) on unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. This paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature

Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems

With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. The paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm, two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements are derived from literature and the application field. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact’s capability to capture selected faults (regarding insulators and safety pins) in unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. The paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators’ specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature

Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

3D Classification of Power Line Scene Using Airborne Lidar Data

Failure to adequately maintain vegetation within a power line corridor has been identified as a main cause of the August 14, 2003 electric power blackout. Such that, timely and accurate corridor mapping and monitoring are indispensible to mitigate such disaster. Moreover, airborne LiDAR (Light Detection And Ranging) has been recently introduced and widely utilized in industries and academies thanks to its potential to automate the data processing for scene analysis including power line corridor mapping. However, today’s corridor mapping practice using LiDAR in industries still remains an expensive manual process that is not suitable for the large-scale, rapid commercial compilation of corridor maps. Additionally, in academies only few studies have developed algorithms capable of recognizing corridor objects in the power line scene, which are mostly based on 2-dimensional classification. Thus, the objective of this dissertation is to develop a 3-dimensional classification system which is able to automatically identify key objects in the power line corridor from large-scale LiDAR data. This dissertation introduces new features for power structures, especially for the electric pylon, and existing features which are derived through diverse piecewise (i.e., point, line and plane) feature extraction, and then constructs a classification model pool by building individual models according to the piecewise feature sets and diverse voltage training samples using Random Forests. Finally, this dissertation proposes a Multiple Classifier System (MCS) which provides an optimal committee of models from the model pool for classification of new incoming power line scene. The proposed MCS has been tested on a power line corridor where medium voltage transmission lines (115 kV and 230 kV) pass. The classification results based on the MCS applied by optimally selecting the pre-built classification models according to the voltage type of the test corridor demonstrate a good accuracy (89.07%) and computationally effective time cost (approximately 4 hours/km) without additional training fees

Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1

The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications