No abstract

How to cite this paper: Sousa, P. H. F.; Nascimento, N. M. M.; Almeida, J. S.; Rebouças Filho, P. P. and Albuquerque, V. AbstractThe eagerness and necessity to develop so-called smart applications has taken the Internet of Things (IoT) to a whole new level. Industry has been implementing services that use IoT to increase productivity as well as management systems over the past couple of years. Such services are now encroaching on wind energy, which nowadays is the most acceptable source among renewable energies for electricity generation. This work proposes an intelligent system to identify incipient faults in the electric generators of wind turbines to improve maintenance routines. Four feature extraction methods were applied to vibration signals, and different classifiers were used to predict the running status of the wind turbine. We correctly identified 94.44% of normal conditions, reducing the false positive and negative rates to 0.4% and 1.84%, respectively; a better result than other approaches already reported in the literature. Kiviluoma [29] analyzed the behavior of the power variation in different regions and demonstrated that rapid changes in local wind conditions cause power fluctuations in the IoT system and operational costs of a wind farm

No abstract

No abstract