Elen Lobato

This study explores the application of Failure Modes and Effects Analysis (FMEA) to ground-mounted and Floating Photovoltaic (FPV) systems, with and without tracking systems, considering the particularities of each configuration. The FMEA methodology was applied to identify and prioritize failure modes, analyzing severity, occurrence and detection, as well as calculating the Risk Priority Number (RPN) and the Action Priority (AP). The FMEA methodology was adopted due to its preventive capability in identifying early-stage component failures and prioritizing actions. To this end, detailed analyses of the main failure modes and effects were carried out, covering critical components such as photovoltaic modules, inverters and support systems. In addition, preventive and corrective actions will be proposed. The study includes the case study of the Research Network for Technological Development and Innovative Extension of Advanced Materials in Energy and Mobility applied to the Amazon context (EMOBAMAZON). This study represents a novel application of FMEA to Floating Photovoltaic (FPV) systems in tropical environments, demonstrating its potential to improve predictive maintenance and operational reliability in renewable energy facilities. The integration of risk classification approaches has enabled a comprehensive and well-founded view of failures, contributing to the reliability, safety and operational efficiency of photovoltaic systems in the Amazon context.

Photovoltaic systems are among the renewable energy sources with the greatest global impact, driven by technologies that enable real-time monitoring, predictive maintenance, and intelligent integration with the electricity grid. In this context, this paper presents the design and implementation of an embedded Internet of Things (IoT) system to monitor temperature and humidity in photovoltaic systems in the Amazon region. The system was implemented in a photovoltaic solar plant located at the Federal University of Pará and used to monitor parameters such as local humidity and temperature, with the latter being considered at three strategic points: the surface of the photovoltaic module exposed to direct solar radiation, the shaded area of the module, and the ambient temperature. The results obtained showed good performance from the embedded system, with emphasis on the ease of remotely updating the embedded system’s code and centralized visualization of the monitored data in an IoT middleware. The device proved to be resistant to the adverse climatic conditions of the Amazon, allowing the operators and managers of the photovoltaic plant to monitor and visualize the measured variables and to draw up preventive and corrective maintenance strategies. In this way, the embedded system designed and implemented is a valuable tool for the photovoltaic plant’s operators and managers, promoting greater energy efficiency, reducing operating costs and increasing the useful life of the modules. It also contributes to the Sustainable Development Goals (SDGs), such as SDG 7 (Clean and affordable energy) and SDG 13 (Climate action).

Among the main problems faced in the context of electric mobility today, the management and monitoring of electric vehicle charging stations, the integration between the diverse types of technologies that make up its architecture, and its low scalability stand out. Therefore, we will present the implementation and complete integration of an electric vehicle charging system in an electric mobility pilot project being executed in the Amazon region in Brazil. Therefore, a literature review of related works will be presented, and its entire implementation will be addressed, from the charging infrastructure, through its back-end system and its Internet of things platform, to its front-end web system for monitoring charging stations. In addition, a complete prototype is created with a real testbed to verify the scalability of the implemented physical system. Based on the testbed evaluations performed, we observe that the implemented system performs well in receiving and sending data from up to 160 electric vehicle charging stations, achieving an average consumption of 26% for CPU and 95% for memory. In addition, it is important to mention that the deployed system supports horizontal scalability, enabling the connection of more charging stations and making it ideal for other integrated systems similar to ours. Based upon the main results obtained with the implemented system, the possibility of carrying out the management and monitoring of charging stations stands out; the integration of different technologies, from the back end and IoT middleware to its front end; a system that supports scalability, enabling the connection of more charging stations; and a reference architecture for charging station management and monitoring systems for the Amazon region.

A pandemia mundial ocasionada pela COVID-19 fez o mundo inovar em várias áreas, principalmente na área da saúde, tendo como uma de suas aliadas à Internet das Coisas (IoT). Assim, os esforços para se contornar os desafios surgidos por conta dessa pandemia, proporcionaram o desenvolvimento de várias aplicações. Um dos desafios surgidos nesta ocasião diz respeito à refrigeração das vacinas para COVID, que precisam ser refrigeradas em uma faixa de temperatura específica. Este desafio já era enfrentado por outros tipos de vacinas, porém, com a pandemia, ele ficou ainda mais notório. Mediante isso, este trabalho irá apresentar o desenvolvimento de um Sistema Refrigerador Monitorado para Vacinação e Amostras Biológicas (SRM-V&A) capaz de monitorar e controlar a temperatura de uma caixa térmica que pode ser utilizada para transportar vacinas, amostras biológicas, testes rápidos e medicamentos. Além disso, o SRM-V&A possui um próprio sistema de web gestão. O sistema criado é destinado para a última fase do transporte até os usuários finais e, durante os testes de funcionamento, atendeu as temperaturas estabelecidos nas normas.