Plataforma Iiot Baseada Em Métodos Computacionais Para Determinação Do Carregamento De Motores Elétricos

• Ano de defesa: 2023
• Autor(a) principal: Santos, Mauri Saraiva dos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Motores de indução trifásicos; Motores elétricos; Análise não invasiva; Eficiência energética; Métodos numéricos; Internet das coisas; IIoT; Non-invasive analysis; Numerical methods; Three-phase induction motors; Energy efficiency
• Link de acesso: http://repositorio.ufc.br/handle/riufc/74900

The quest for sustainable and efficient solutions has taken precedence across various sectors within the industrial landscape, owing to the burgeoning interest in environmentally responsible practices. Within this context, energy efficiency has emerged as a pivotal factor in shaping of a more environmentally sustainable industry. On the other hand, the Industrial Internet of Things (IIoT) approach has yielded substantial strides in monitoring and controlling industrial processes, including load analysis on three-phase electric motors, which constitute a significant portion of energy consumption. Nonetheless, implementing large-scale monitoring systems still encounters challenges linked to the costs associated with conventional measuring devices. In this dissertation, an IIoT platform is presented that offers a practical alternative in gathering important information in the field of energy efficiency of Three-Phase Induction Motor (3-phase IM) in operation. The platform allows continuous and non-invasive analysis of the loading of industrial driving processes, reducing the need for stops due to unscheduled interventions. The results demonstrated that the platform successfully achieved the proposed objectives, integrating hardware devices, the freely accessible Application Programming Interface (API) developed in Node-Red and the MATLAB® executable script using the Cloud MQTT message broker. The platform was used to compare the load curves stratified through bench tests with the loads estimated by the linearization methods and by the Gauss-Seidel and Newton-Raphson methods, applied to two engines with a power of 1.5 hp and 10 hp, showing that the iterative numerical methods produced results with percentage errors below 10% for loading values between 75% and 150% in the two 3-phase IMs, standing out in relation to the current linearization method. It is concluded that the IIoT platform developed achieved the objectives of this dissertation, presenting a solution for analyzing 3-phase IM load in real time, with low cost and non-invasive.