Main Article Content
Due to the increasing unit capacity of wind turbines and development in wind power technology, the installed capacity is increased each year which leads to the poor working condition of the turbine and reduced transmission due to load changing complexity. Furthermore, there are several problems with the wind turbines due to the high force of wind blow, the blades may get damaged due to high vibration which results in the fatigue of main body of the wind turbine. In this paper, the wind turbine has been monitor using the integrated model using three stages such as preprocessing, fog computing, and fault classification. The secured data stored in the cloud is analyzed so as to provide an effective power generation so as to meet future demands. Moreover, it has been observed that the data undergoes a variation over the power generation due to fault detection caused by generation bearing, tip speed ratio noise, etc. The performance of the proposed method is analyzed by various parameters such as model performance.