Sistema de Detecção de Falhas em Geradores de Indução Duplamente Alimentados

Abstract

The world is increasingly focused on more sustainable and environmentally friendly energy sources. In this context, wind energy has been gaining prominence. To ensure a continuous and accessible energy supply to consumers, it is essential that wind turbines constantly improve their performance. A fundamental component of these turbines is the Doubly Fed Induction Generator (DFIG). This work aims to study ways to model and diagnose the health of the DFIG, focusing on condition-based preventive maintenance (CBM) to enhance the overall availability of wind turbines. In an initial analysis, the application of CBM techniques to the DFIG can increase both availability and electrical safety for consumers.