173 research outputs found
A survey of sag monitoring methods for power grid transmission lines
The transmission line is a fundamental asset in the power grid. The sag condition of the transmission line between two support towers requires accurate real-time monitoring in order to avoid any health and safety hazards or power failure. In this paper, state-of-the-art methods on transmission line sag monitoring are thoroughly reviewed and compared. Both the direct methods that use the direct video or image of the transmission line and the indirect methods that use the relationships between sag and line parameters are investigated. Sag prediction methods and relevant industry standards are also examined. Based on these investigation and examination, future research challenges are outlined and useful recommendations on the choices of sag monitoring methods in different applications are made
Conductor minimum safe distance analysis: Application of a 20 kV medium voltage airline (SUTM) system
Medium Voltage Air Line Conductor (SUTM) has a voltage of 20 kV. The SUTM network should have the criteria for electricity safety techniques, including minimum safety distances between the trees and the environment and the effectiveness of electricity distribution development. There are ten stages in the installation of the new SUTM 20 kV network. The results of the study concluded that the Conductor used in the planning of the 20 kV SUTM new network construction was AAACS - 150 mm2. The safe distance between the conductors and the conditions contained Billboards are 0.5 meters with a minimum height difference of ± 2.5 meters. Whereas the safe distance between the conductor and the tree is ± 0.5 meters, but the Medium Voltage Network Construction Standard for Electric Power must have a height difference of 2.5 meters. The distance between the conductor and billboards is 0.5 meters, which does not complete the standard instructions
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Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data
Paper describes the development and testing of a three-dimensional reconstruction method using airborne LiDAR (Light Detection And Ranging) point cloud
Detection of Power Line Supporting Towers via Interpretable Semantic Segmentation of 3D Point Clouds
The inspection and maintenance of energy transmission networks are demanding and
crucial tasks for any transmission system operator. They rely on a combination of on-theground
staff and costly low-flying helicopters to visually inspect the power grid structure.
Recently, LiDAR-based inspections have shown the potential to accelerate and increase
inspection precision. These high-resolution sensors allow one to scan an environment and
store it in a 3D point cloud format for further processing and analysis by maintenance
specialists to prevent fires and damage to the electrical system. However, this task is
especially demanding to handle on time when we consider the extensive area that the
transmission network covers. Nonetheless, the transition to point cloud data allows us to
take advantage of Deep Learning to automate these inspections, by detecting collisions
between the grid and the revolving scene.
Deep Learning is a recent and powerful tool that has been successfully applied to a
myriad of real-life problems, such as image recognition and speech generation. With the
introduction of affordable LiDAR sensors, the application of Deep Learning on 3D data
emerged, with numerous methods being proposed every day to address difficult problems,
from 3D object detection to 3D point cloud segmentation. Alas, state-of-the-art methods
are remarkably complex, composed of millions of trainable parameters, and take several
weeks, if not months, to train on specific hardware, which makes it difficult for traditional
companies, like utilities, to employ them.
Therefore, we explore a novel mathematical framework that allows us to define tailored
operators that incorporate prior knowledge regarding our problem. These operators
are then integrated into a learning agent, called SCENE-Net, that detects power line supporting
towers in 3D point clouds. SCENE-Net allows for the interpretability of its results,
which is not possible in conventional models, it shows an efficient training and inference
time of 85 mn and 20 ms on a regular laptop. Our model is composed of 11 trainable
geometrical parameters, like the height of a cylinder, and has a Precision gain of 24%
against a comparable CNN with 2190 parameters.A inspeção e manutenção de redes de transmissão de energia são tarefas cruciais para
operadores de rede. Recentemente, foram adotadas inspeções utilizando sensores LiDAR
de forma a acelerar este processo e aumentar a sua precisão. Estes sensores são objetos de
alta precisão que conseguem inspecionar ambientes e guarda-los no formato de nuvens
de pontos 3D, para serem posteriormente analisadas por specialistas que procuram prevenir
fogos florestais e danos à estruta eléctrica. No entanto, esta tarefa torna-se bastante
difícil de concluir em tempo útil pois a rede de transmissão é bastasnte vasta. Por isso,
podemos tirar partido da transição para dados LiDAR e utilizar aprendizagem profunda
para automatizar as inspeções à rede.
Aprendizagem profunda é um campo recente e em grande desenvolvimento, sendo
aplicado a vários problemas do nosso quotidiano e facilmente atinge um desempenho
superior ao do ser humano, como em reconhecimento de imagens, geração de voz, entre
outros. Com o desenvolvimento de sensores LiDAR acessíveis, o uso de aprendizagem
profunda em dados 3D rapidamente se desenvolveu, apresentando várias metodologias
novas todos os dias que respondem a problemas complexos, como deteção de objetos
3D. No entanto, modelos do estado da arte são incrivelmente complexos e compostos
por milhões de parâmetros e demoram várias semanas, senão meses, a treinar em GPU
potentes, o que dificulta a sua utilização em empresas tradicionais, como a EDP.
Portanto, nós exploramos uma nova teoria matemática que nos permite definir operadores
específicos que incorporaram conhecimento sobre o nosso problema. Estes operadores
são integrados num modelo de aprendizagem prounda, designado SCENE-Net,
que deteta torres de suporte de linhas de transmissão em nuvens de pontos. SCENE-Net
permite a interpretação dos seus resultados, aspeto que não é possível com modelos convencionais,
demonstra um treino eficiente de 85 minutos e tempo de inferência de 20
milissegundos num computador tradicional. O nosso modelo contém apenas 11 parâmetros
geométricos, como a altura de um cilindro, e demonstra um ganho de Precisão de
24% quando comparado com uma CNN com 2190 parâmetros
Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool
Computer programming and IoT are the key skills required in Industrial
Revolution 4.0 (IR4.0). The industry demand is very high and therefore related
students in this field should grasp adequate knowledge and skill in college or university
prior to employment. However, learning technology related subject without
applying it to an actual hardware can pose difficulty to relate the theoretical knowledge
to problems in real application. It is proven that learning through hands-on
activities is more effective and promotes deeper understanding of the subject matter
(He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education:
Case study of a modern technology infused courseware for embedded system
course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an
integrated learning tool that combines learning of computer programming and IoT
control for an industrial liquid filling system model is developed and tested. The
integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the
IoT application. The system set-up is pre-designed for semi-automation liquid filling
process to enhance hands-on learning experience but can be easily programmed for
full automation. Overall, it is a user and cost friendly learning tool that can be developed
by academic staff to aid learning of IoT and computer programming in related
education levels and field
Real-Time LiDAR-based Power Lines Detection for Unmanned Aerial Vehicles
The growing dependence of modern-day societies on electricity leads to the increasing importance of effective monitoring and maintenance of power lines. Due to the population’s renouncement to the installation of new electric power lines, the existing ones are constantly operating at maximum capacity. This leaves no room for breakdowns, as it leads to major economic losses for the electrical companies and blackouts for the consumers. Endowing Unmanned Aerial Vehicles (UAVs) with the appropriate sensors for inspection the power lines, the costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced. However, this implies the development of algorithms to make the inspection process reliable and autonomous. Visual detection methods are usually applied to locate the power lines and their components. Although, they are generally too sensitive to atmospheric conditions and noisy background. Poor light conditions or a background rich in edges may compromise their results. In order to overcome those limitations, this dissertation addresses the problem of power line detection and modeling based on the use of a Light Detection And Ranging (LiDAR) sensor. A novel approach to the power line detection was developed, the Power Line LiDARbased Detection and Modeling (PL2DM). It is based in a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. In the segmentation, the breaking cluster points are detected by an analysis of their planar properties. Exporting the potential power line points to a further step, it performs a scan based straight line detection. The final model of the power line is obtained by matching and grouping the several line segments detected using their collinearity properties. Horizontally, the power lines are modeled as a straight line, while vertically are approximated to a catenary curve.
The algorithm was tested with a real dataset, showing promising results both in terms of outputs and processing time. From there, it was demonstrated that the proposed algorithm can be applied to real-time operations of the UAV, adding object-based perception capabilities for other layers of processing.A crescente dependência das sociedades modernas no uso de eletricidade conduz a uma crescente importância da eficiência da monitorização e manutenção das linhas elétricas. A renitência das populações `a instalação de novas linhas elétricas faz com que as existentes estejam constantemente a operar na sua máxima capacidade. Isto faz com que não possam existir falhas, uma vez que resultariam em grandes perdas económicas para as companhias elétricas e em falhas energéticas para os consumidores. Equipando um Unmanned Aerial Vehicle (UAV) com os sensores adequados `a inspeção de linhas elétricas, podem ser reduzidos os custos e riscos de operação associados `as inspeções tradicionais, baseadas em patrulhas pedonais e no uso de um helicóptero. No entanto, isto implica o desenvolvimento de algoritmos para que o processo de inspeção seja fiável e autónomo. As linhas elétricas e os componentes associados são geralmente localizados através de métodos de deteção visual. Estes m´métodos são, geralmente, muito sensíveis `as condições atmosféricas e a fundos ruidosos. Condições de luz deficientes ou fundos ricos em contrastes são alguns dos fatores que podem comprometer os seus resultados. De forma a ultrapassar essas limitações, esta dissertação endereça o problema da deteção e modelação de linhas elétricas, tendo por base o uso de um sensor Light Detection And Ranging (LiDAR). Foi desenvolvida uma nova abordagem aos métodos de deteção de linhas elétricas, o Power Line LiDAR-based Detection and Modeling (PL2DM). Esta abordagem ´e baseada numa análise individual de varrimentos, em que ´e feita uma comparação minimalista de todos os pontos, presentes numa dada nuvem de pontos, com uma vizinhança adaptativa. Na segmentação, os pontos de quebra dos grupos criados são detetados tendo em conta as suas propriedades planares. Passando os pontos passíveis de pertencerem a linhas elétricas para o processamento seguinte, é realizada, em cada varrimento, uma deteção de linhas retas. O modelo final das linhas elétricas é obtido a partir da associação e agrupamento dos diversos segmentos de reta detetados, tendo por base a sua colinearidade. Na sua projeção horizontal, as linhas elétricas são modeladas como linhas retas. Verticalmente, são aproximadas ao modelo de uma curva catenária. O algoritmo foi testado com um conjunto de dados reais, tendo mostrado resultados promissores, tanto em termos de dados gerados como de tempo de processamento. Com isso, ficou demonstrado que o algoritmo proposto pode ser aplicado nas operações do UAV em tempo real, adicionando capacidades de perceção baseada em objetos para outras camadas de processamento
NASA Tech Briefs Index, 1977, volume 2, numbers 1-4
Announcements of new technology derived from the research and development activities of NASA are presented. Abstracts, and indexes for subject, personal author, originating center, and Tech Brief number are presented for 1977
Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021
This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI)
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