Trabalho de Conclusão de Curso de Graduação
Detecção de desbalanceamento de massa entre pás de aerogerador utilizando sinais elétricos de um gerador síncrono de imãs permanentes aplicados a métodos de aprendizagem de máquina
Fecha
2019-12-02Registro en:
ROSA, L. D. da. Detecção de desbalanceamento de massa entre pás de aerogerador utilizando sinais elétricos de um gerador síncrono de imãs permanentes aplicados a métodos de aprendizagem de máquina. 2019. 84 p. Trabalho de Conclusão de Curso (Graduação em Engenharia de Controle e Automação)- Universidade Federal de Santa Maria, Santa Maria, RS, 2019.
Autor
Rosa, Leonardo Dias da
Institución
Resumen
The use of wind power has increased in electricity production due to its cost becoming
closer to conventional generation sources. The useful lifetime expectancy of wind turbines
are typically of 20 years, however failures are expected much earlier due to components
(especially gears, electrical system and blades) failures. Detecting and avoiding harsh
operations and conditions of wind turbines are useful to reduce downtimes and catastrophic
failures that could lead to greater losses and even a complete shutdown of the
turbine. A considerable amount of failures in components can be assign to the rotor
imbalance of wind turbines, since the imbalance can cause greater mechanical loads throughout
the whole structure. In this paper, machine learning methods are applied in
order to detect blade mass imbalance in wind turbines. The data used for such task were
obtained from the platform Turbsim/FAST/Simulink, which simulates the dynamics of
a 1.5 MW wind turbine, with both normal and blade mass imbalance operations. The
electrical quantities obtained from the permanent magnets synchronous generator of the
wind turbine allows to estimate the rotational speed of the rotor, and this data is applied
to machine learning methods to detect whether there is imbalance or not. The use of
coupled aeroelastic numeric simulations and machine learning in this paper presented a
feasible method to improve the production of wind power, reducing downtimes, allowing
scheduled maintenances and increasing the LCOE of wind power generation.