Routing algorithm for the ground team in transmission line inspection using unmanned aerial vehicle

With the rapid development of robotics technology, robots are increasingly used to conduct various tasks by utility companies. An unmanned aerial vehicle (UAV) is an efficient robot that can be used to inspect high-voltage transmission lines. UAVs need to stay within a data transmission range from the ground station and periodically land to replace the battery in order to ensure that the power system can support its operation. A routing algorithm must be used in order to guide the motion and deployment of the ground station while using UAV in transmission line inspection. Most existing routing algorithms are dedicated to pathfinding for a single object that needs to travel from a given start point to end point and cannot be directly used for guiding the ground station deployment and motion since multiple objects (i.e., the UAV and the ground team) whose motions and locations need to be coordinated are involved. In this thesis, we intend to explore the routing algorithm that can be used by utility companies to effectively utilize UAVs in transmission line inspection. Both heuristic and analytical algorithms are proposed to guide the deployment of the ground station and the landing point for UAV power system change. A case study was conducted to validate the effectiveness of the proposed routing algorithm and examine the performance and cost-effectiveness --Abstract, page iii

Safe local aerial manipulation for the installation of devices on power lines: Aerial-core first year results and designs

Article number 6220The power grid is an essential infrastructure in any country, comprising thousands of kilometers of power lines that require periodic inspection and maintenance, carried out nowadays by human operators in risky conditions. To increase safety and reduce time and cost with respect to conventional solutions involving manned helicopters and heavy vehicles, the AERIAL-CORE project proposes the development of aerial robots capable of performing aerial manipulation operations to assist human operators in power lines inspection and maintenance, allowing the installation of devices, such as bird flight diverters or electrical spacers, and the fast delivery and retrieval of tools. This manuscript describes the goals and functionalities to be developed for safe local aerial manipulation, presenting the preliminary designs and experimental results obtained in the first year of the project.European Union (UE). H2020 871479Ministerio de Ciencia, Innovación y Universidades de España FPI 201

A survey of electromagnetic influence on uavs from an ehv power converter stations and possible countermeasures

National Natural Science Foundation of China (Grant Nos. 11872148, U1908217, 61801034).It is inevitable that high-intensity, wide-spectrum electromagnetic emissions are generated by the power electronic equipment of the Extra High Voltage (EHV) power converter station. The surveillance flight of Unmanned Aerial Vehicles (UAVs) is thus, situated in a complex electromagnetic environment. The ubiquitous electromagnetic interference demands higher electromagnetic protection requirements from the UAV construction and operation. This article is related to the UAVs patrol inspections of the power line in the vicinity of the EHV converter station. The article analyzes the electromagnetic interference characteristics of the converter station equipment in the surrounding space and the impact of the electromagnetic emission on the communication circuits of the UAV. The anti-electromagnetic interference countermeasures strive to eliminate or reduce the threats of electromagnetic emissions on the UAV’s hardware and its communication network.publishersversionpublishe

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

Thesis title: Feasibility study of using collaborative UAVs for Emergency Response in Road Tunnels

Utilizing UAVs for assisting in emergency response missions is already a fact, both in open landscapes like forests and in restricted areas like sewers. However, using them in road tunnels has not yet been realised, and could possibly provide a huge help for the first responders in the form of surveillance, providing network coverage or announcing self-help assistance to the victims. There are certain challenges for this to be possible, some of them being lack of signal coverage, battery life and positional navigation in a GPS-denied environment. In this thesis the feasibility of this will be put into consideration by surveying available software and hardware for this utilization, as well as setting up a generalised energy consumption model to check where the different drone configurations can be used. The results implies that the state-of-the-art drone configurations are very capable of being used to assist in emergency situations in road tunnels, both when it comes to response time and length coverage. However, the main restricting factor will be cost, as modern drone swarm configurations with a reasonable battery capacity and sophisticated sensors comes at a high cost.Utilizing UAVs for assisting in emergency response missions is already a fact, both in open landscapes like forests and in restricted areas like sewers. However, using them in road tunnels has not yet been realised, and could possibly provide a huge help for the first responders in the form of surveillance, providing network coverage or announcing self-help assistance to the victims. There are certain challenges for this to be possible, some of them being lack of signal coverage, battery life and positional navigation in a GPS-denied environment. In this thesis the feasibility of this will be put into consideration by surveying available software and hardware for this utilization, as well as setting up a generalised energy consumption model to check where the different drone configurations can be used. The results implies that the state-of-the-art drone configurations are very capable of being used to assist in emergency situations in road tunnels, both when it comes to response time and length coverage. However, the main restricting factor will be cost, as modern drone swarm configurations with a reasonable battery capacity and sophisticated sensors comes at a high cost