Outdoor Insulation and Gas Insulated Switchgears

This book focuses on theoretical and practical developments in the performance of high-voltage transmission line against atmospheric pollution and icing. Modifications using suitable fillers are also pinpointed to improve silicone rubber insulation materials. Very fast transient overvoltage (VFTO) mitigation techniques, along with some suggestions for reliable partial discharge measurements under DC voltage stresses inside gas-insulated switchgears, are addressed. The application of an inductor-based filter for the protective performance of surge arresters against indirect lightning strikes is also discussed

Remote Sensing methods for power line corridor surveys

AbstractTo secure uninterrupted distribution of electricity, effective monitoring and maintenance of power lines are needed. This literature review article aims to give a wide overview of the possibilities provided by modern remote sensing sensors in power line corridor surveys and to discuss the potential and limitations of different approaches. Monitoring of both power line components and vegetation around them is included. Remotely sensed data sources discussed in the review include synthetic aperture radar (SAR) images, optical satellite and aerial images, thermal images, airborne laser scanner (ALS) data, land-based mobile mapping data, and unmanned aerial vehicle (UAV) data. The review shows that most previous studies have concentrated on the mapping and analysis of network components. In particular, automated extraction of power line conductors has achieved much attention, and promising results have been reported. For example, accuracy levels above 90% have been presented for the extraction of conductors from ALS data or aerial images. However, in many studies datasets have been small and numerical quality analyses have been omitted. Mapping of vegetation near power lines has been a less common research topic than mapping of the components, but several studies have also been carried out in this field, especially using optical aerial and satellite images. Based on the review we conclude that in future research more attention should be given to an integrated use of various data sources to benefit from the various techniques in an optimal way. Knowledge in related fields, such as vegetation monitoring from ALS, SAR and optical image data should be better exploited to develop useful monitoring approaches. Special attention should be given to rapidly developing remote sensing techniques such as UAVs and laser scanning from airborne and land-based platforms. To demonstrate and verify the capabilities of automated monitoring approaches, large tests in various environments and practical monitoring conditions are needed. These should include careful quality analyses and comparisons between different data sources, methods and individual algorithms

Aerial Image Analysis using Deep Learning for Electrical Overhead Line Network Asset Management

Electricity networks are critical infrastructure, delivering vital energy services. Due to the significant number, variety and distribution of electrical network overhead line assets, energy network operators spend millions annually on inspection and maintenance programmes. Currently, inspection involves acquiring and manually analysing aerial images. This is labour intensive and subjective. Along with costs associated with helicopter or drone operations, data analysis represents a significant financial burden to network operators. We propose an approach to automating assessment of the condition of electrical towers. Importantly, we train machine learning tower classifiers without using condition labels for individual components of interest. Instead, learning is supervised using only condition labels for towers in their entirety. This enables us to use a real-world industry dataset without needing costly additional human labelling of thousands of individual components. Our prototype first detects instances of components in multiple images of each tower, using Mask R-CNN or RetinaNet. It then predicts tower condition ratings using one of two approaches: (i) component instance classifiers trained using class labels transferred from towers to each of their detected component instances, or (ii) multiple instance learning classifiers based on bags of detected instances. Instance or bag class predictions are aggregated to obtain tower condition ratings. Evaluation used a dataset with representative tower images and associated condition ratings covering a range of component types, scenes, environmental conditions, and viewpoints. We report experiments investigating classification of towers based on the condition of their multiple insulator and U-bolt components. Insulators and their U-bolts were detected with average precision of 96.7 and 97.9, respectively. Tower classification achieved areas under ROC curves of 0.94 and 0.98 for insulator condition and U-bolt condition ratings, respectively. Thus we demonstrate that tower condition classifiers can be trained effectively without labelling the condition of individual components

Deep Learning Approach for UAV Visual Electrical Assets Inspection

The growth in the electrical demand by most countries around the world requires bigger and more complex energy systems, which leads to the requirement of having even more monitoring, inspection and maintenance of those systems. To respond to this need, inspection methods based on Unmanned Aerial Vehicles (UAV) have emerged which, when equipped with the appropriate sensors, allow a greater reduction of costs and risks and an increase in efficiency and effectiveness compared to traditional methods, such as inspection with foot patrols or helicopter-assisted. To make the inspection process more autonomous and reliable, most of the methods apply visual detection methods that use highly complex Deep Learning based algorithms and that require a very large computational power. This dissertation intends to present a system for inspection of electrical assets, able to be integrated onboard the UAV, based on Deep Learning, which allows to collect visual samples grouped and aggregated for each electrical asset detected. To this end, a perception system capable of detecting electrical insulators or structures, such as poles or transmission towers, was developed, using the Movidius Neural Compute Stick portable platform that is capable of processing lightweight object detection Convolutional Neural Networks, allowing a modular, low-cost system that meets real-time processing requirements. In addition to this perception system, an electrical asset monitoring system has been implemented that allows tracking and mapping each asset throughout the inspection process, based on the previous system’s detections and a UAV navigation system. Finally, an autonomous inspection system is proposed, which consists of a set of trajectories that allow an efficient application of the monitoring system along a power line, through the mapping of structures and the gathering of insulator samples around that structure.O grande crescimento da exigência elétrica pela maioria dos países por todo o mundo, requer que os sistemas de energia sejam maiores e mais complexos, o que conduz a uma maior necessidade de monitorização, inspeção e manutenção desses sistemas. Para responder a esta necessidade, surgiram métodos de inspeção baseados em Veículos Aéreos Não Tripulados (VANT) que, quando equipados com os sensores apropriados, permitem uma maior redução de custos e riscos e um grande aumento de eficiência e eficácia em comparação com os métodos tradicionais, como a inspeção com patrulhas pedonais ou assistida por helicóptero. Para tornar processo de inspeção mais autónomo e confiável, a maioria dos métodos realiza método de deteção visuais que utilizam algoritmos baseados em Deep Learning de elevada complexidade e que requerem um poder computacional muito grande. Nesta dissertação pretende-se apresentar um sistema de inspeção de ativos elétricos, para integração em VANTs, baseado em Apredizagem Profunda, que permite recolher amostras visuais agrupadas e agregadas por cada ativo elétrico detetado. Para tal foi desenvolvido um sistema de perceção capaz de detetar isoladores elétricos ou estruturas, como postes ou torres de transmissão, com recurso `a plataforma portátil Movidius Neural Compute Stick que ´e capaz de processar Redes Neuronais Convolucionais leves de deteção de objetos, permitindo assim um sistema modular, de baixo custo e que cumpre requisitos de processamento em tempo real. Para além deste sistema de perceção, foi implementado um sistema de monitorização de ativos elétricos que permite seguir e mapear cada ativo ao longo do processo de inspeção, com base nas deteções do sistema anterior e no sistema de navegação do VANT. Por fim, ´e proposto um sistema de inspeção autónomo que consiste num conjunto de trajetórias que permitem aplicar o sistema de monitorização de ativos elétricos ao longo de uma linha elétrica, através do mapeamento de estruturas e na recolha de amostras de isoladores em torno dessa estrutura

Automatic vision based fault detection on electricity transmission components using very highresolution

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations. Fault identification is one of the most significant bottlenecks faced by Electricity transmission and distribution utilities in developing countries to deliver credible services to customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial resolution to monitor four major Electric power transmission network (EPTN) components condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks (CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage object detection model on the electric transmission power line imagery to localize, classify and inspect faults present. The components fault considered include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision of 89.61%. All the developed SSD based models achieve a high precision rate and low recall rate in detecting the faulty components, thus achieving acceptable balance levels F1-score and representation. Finally, comparable to other works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection and their component fault mapping in the long - run if these deep learning architectures are widely understood, adequate training samples exist to represent multiple fault characteristics; and the effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale datasets are clearly understood

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

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