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Item Modelagem e Simulação de Indicadores de Continuidade: Ferramenta Auxiliar para a Manutenção em Redes de Distribuição de Energia Elétrica(2017) Magalhães, Emerson Felipe Araujo; Costa Filho, Marcus Vinícius Americano da; Magalhães, Robson da Silva; Pontes, Karen Valverde; Ávila Filho, Salvador; Oliveira, Tiago Cordeiro deThe reliability of the electricity distribution systems is associated with the many investments that are implemented, specifically when the design of new projects and during the programming of maintenance activities. The Brazilian electric sector has been experiencing countless problems over time, with the greatest of these problems related to the constant failures in the distribution system. Occurrences of these failures influence the composition of the continuity indicators of Equivalent Duration of Unit Interruption Consumer (DEC), and Equivalent Frequency of Interruption of Unit Interruption Consumer (FEC); which are established by the National Electric Energy Agency (ANEEL), for each concessionaire, and if they are not fulfilled, the concessionaires will be subject to fines. This work proposes the development of models, using Multiple Linear Regression techniques (MLR) and Artificial Neural Networks (ANNs), which predict the values of these continuity indicators, aiming to assist the concessionaire in carrying out a more efficient planning for performing maintenance tasks. As an object of study, a collection of consumers was used from a concessionaire in the Northeast region. From the modes of failures occurring in the circuits that feed this set of consumers, the causes and frequencies of interruption in the electricity supply are analyzed, and how much these contribute to the increase in the values of DEC and FEC. Through the application of the developed models, you can evaluate the influence of certain modes of failures on DEC and FEC, prioritizing maintenance tasks in the equipment that contribute to the composition of these indicators. It is demonstrated that the knowledge of the influence of the modes of failures on the DEC and FEC indicators allows a proper decision-making on the implementation of maintenance actions, enabling the concessionaire to perform an efficient operation on those modes of flaws that are more and contributing to the operational stability and reliability of the entire electrical system.Item Modelo para predição de indicadores de continuidade em um sistema de distribuição de energia elétrica, uma aplicação à gestão de manutenção com a perspectiva do uso da termografia(2016) Araújo Junior, José Arnóbio de; Magalhães, Robson da Silva; Esquerre, Karla Patrícia Santos Oliveira Rodriguez; Bandeira, Anselmo Alves; Silva, Magno José Gomes daThe Electrical power distribution system (SDEE) of most of the Northeast region concessionaires are old and present different kinds of problems. In an indirect way, these problems can be verified through the collective indicators of continuity, provided by the National Electric Energy Agency (ANEEL). These indicators include the Interruption Equivalent Duration per Consumer Unit (DEC) and the Interruption Equivalent Frequency per Consumer Unit (FEC). In order to improve these indicators, this study proposes a methodology to be applied in the analysis of the history of occurrences (interruptions in power supply) registered in an electricity distribution concessionaire, from January 2013 to December 2014. The methodology proposed here is based on the construction of a model that establishes the DEC and FEC indicators. Once the proposed model is constructed, considering the period of operation of the SDEE to be analyzed, we intend to set the values of DEC and FEC, according to the observed and recorded occurrences for this system. The prior knowledge of DEC and FEC indicators allows the maintenance management, making it possible to measure the influence of the main failure modes on the composition of these indicators. As a basis for the elaboration of the analysis methodology and that of the model, we carried out a research, together with an energy distribution concessionaire, which will be denominated, in this work, as concessionaire A. This research involves a survey of the occurrences of failures (with interruption) within a group of consumers. Data was collected monthly, from January 2013 to December 2014. From these data, a model structure based on Multiple Linear Regression Analysis (ARLM) was constructed. There is, on the part of concessionaire A, the intention to develop maintenance actions that apply thermographic techniques. These techniques allow the thermal gradient mapping of a given energized device, distinguishing different temperatures by means of infrared radiation. For this purpose, the concessionaire A has invested on equipment and personnel training. This study proposes the application of the model which was built, based on ARLM, as a support tool to evaluate the influence of failure modes on the composition of continuity indicators (DEC and FEC). The failure modes of greatest interest are those that are likely to be detected by thermography. The prior knowledge of the influence of failure modes on the composition of these indicators will enable a decision making, aiming at the execution of maintenance actions, allowing the concessionaire to perform a direct action on the feeder and on the most influential modes of failure, especially on those that can be detected by thermography.Item Sistema de computação em borda para controle preditivo de veículos autoguiados em redes sem fio sujeitas à degradação(2023) Omena, Rômulo Afonso Luna Vianna de; Santos, Danilo Freire de Souza; Perkusich, Angelo; Lima, Antonio Marcus Nogueira; Silva, Jaidilson Jó da; Brito, Alisson Vasconcelos de; Pereira, Carlos Eduardo; Valadares, Dalton Cézane GomesAutomated Guided Vehicles (AGVs) are essential for industry material transportation. In the Industry 4.0 and Industrial Internet of Things scenario, the AGV fleet is expected to be connected and integrated into the factory management system, being flexible and adapting to new demands. AGV control systems with fixed path navigation may not meet these requirements. Edge computing brings cloud resources to the network’s edge, making them closer to users. These resources can be accessed through a wireless network and applied to industrial demands. The AGVs can benefit from this when offloading tasks that require more computing resources to the edge server. However, the wireless network in the industrial environment is subject to degradation due to interference, signal reflections, shadowing effects, and electromagnetic wave absorption, among other challenges. The AGV, as a mobile robot, may traverse areas where the signal is degraded, increasing risks of collisions and accidents. Results of experiments suggest that Model Predictive Control (MPC) executed at the edge server, combined with a delay and packet loss compensation strategy implemented in the robot, can be used to mitigate these network degradations. In sequence, a two-tier architecture with MPCs is proposed to control multiple AGVs. The first tier, executed on the edge server, plans the trajectory of the AGVs globally, preventing collisions of the AGVs with fixed obstacles and each other. In the computer embedded in the AGV, the compensator used in the previous experiments gives place to a trajectory-tracking MPC, which must receive the trajectory of the respective AGV from the edge server and track it. Results of experiments carried out in four validation scenarios indicate that from the proposed architecture, it is possible to drive the AGVs without collisions, even in the communication network’s occurrence of delays and packet losses. In addition, tasks that demand more computational resources are offloaded to the edge server so that the computer embedded in the AGV can have more restricted resources, reducing costs and battery consumption.