Abstract
In recent years there has been an increase in wind generation, driven by environmental factors and the incentive offered for the development of clean and sustainable technologies for energy generation. However, due to the rapid growth of this technology, concerns about the safety and reliability of wind turbines are increasing, especially due to the associated risks and financial costs. Therefore, health monitoring and fault detection for wind turbines has become an important research focus. Thus, the objective of this work was to realize an exploratory study of real data from a wind turbine, using AI tools that help to group the different behaviors, according to the similarity of resources and characteristics of the data. For this, unsupervised learning methods were used to cluster the data and a model was proposed to train and test, using a multilayer perceptron network, to classify these clusters. The differential of this work is the use of real data from CHESF's wind turbines. Another important contribution is in relation to permanent magnet wind turbines, as there are not many studies in this field, therefore a great potential to be explored.