International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 04 | Apr 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3582 DESIGN THINKING ON FAULT DIAGNOSIS OF FLOATING WIND TURBINE GENERATOR USING ARTIFICIAL INTELLIGENCE Dr.B.Sivasankari 1 , G.Sathish Kumar 2 , R.Prakash 3 , S.Sanjanaa 4 , N.Subhashree 5 1 Associate Professor, Dept of Electronics and Communication Eng, SNS College Of Technology Coimbatore, Tamil Nadu- India. 4 UG Student, Dept of Electronics and Communication Eng, SNS College Of Technology Coimbatore, Tamil Nadu-India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Floating wind turbines are the centerpiece of offshore floating power generation and an important means of developing offshore wind resources. In few years, artificial intelligence technology has achieved great results in many fields, and equipment fault diagnosis is also much important. Since the working environment of the floating fan is bad and far from the land, a failure of the floating fan generator can have very serious consequences. Based on the research results of AI technology, this article focuses on the application of AI technology in fault diagnosis of floating wind turbines and proposes a fault diagnosis intelligent system framework. According to the research of expert systems and artificial neural networks, the floating fan generator failure diagnosis technology is proposed, and the artificial neural network model and the reasonable failure receive the diagnostic reasoning flow. Key Words: artificial intelligence; component; neural network; fault diagnosis; floating wind turbine. 1. INTRODUCTION The generator is the heart of the offshore floating wind turbine, and its proper performance is the foundation for the wind turbines proper operation. Because the floating fans are situated in the water and are unattended, the unit frequently fails due to the poor operating environment, making the intelligent generator fault detection system extremely vital. Overload, bearing fracture, line ageing, and other common generator defects are all too common. When a malfunction arises that cannot be corrected in a timely manner, the light will force the unit to shut down, while the heavy will burn the entire unit, resulting in huge financial losses. Furthermore, failure of one component might influence other components or even the complete wind turbine in extreme circumstances. It is significant in light of all of these facts. Using this technology, it will greatly help the physically challenged and elderly people to control the appliances easily. A mobile application along with Vuforia Server will help the users to control a switch by simply pointing their mobile camera to it from a distance. Different virtual switches will appear like on and off when the camera is pointed to image target, thus allow the user to control different appliances easily and conveniently. Instead of normal switches, 2D buttons will appear on the screen which gives a familiar interface to the user. When the user touches the button information will be transfer to the Blynk server and instruction is forwarded to the microcontroller and the controller turns the device on or off according to the user operation. Fig -1: Wind Turbine Components