Wind power generation fault diagnosis based on deep learning model in internet of things (IoT) with clusters

With the rapid development of wind power capacity and sustained growth in total operation time, the maintenance of wind turbines is becoming increasingly prominent, so we urgently need to develop effective wind turbine fault diagnosis and prediction system. The main fault characteristics of wind turbines are summarized from two aspects of fault diagnosis and fault prediction. Aiming at the difficult problems of fault diagnosis, we analyze and summarize the research status of fault diagnosis methods based on vibration, electrical signal analysis and pattern recognition algorithm. At the same time, we point out the technical characteristics, limitations and future trends of various methods. Based on the characteristics of mechanical structure and electronic system degradation in wind turbines, we summarize the current research progress and propose a fault prediction method based on physical failure model and data driven model fusion. In this paper, we use the deep learning model in the framework of the internet of things to predict and diagnose the faults of wind power generation. The experimental results show that the algorithm proposed in this paper can predict the fault types and make reasonable diagnosis.