Emerging trends and global challenges to predict drop in thermal performance of WTG gearbox
1Department of Mechanical Engineering, Faculty of Engineering, Mohamed Sathak A.J. College of Engineering, IT park, Siruseri, Chennai, 603103, India
2Department of Mechanical Engineering, Lendi Institute of Engineering and Technology Jonnada, Vizinagaram, Andhrapradesh, 535005, India
3Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India
4Department of Electrical and Electronics Engineering, Christ the King Engineering College, Karamadai, Coimbatore, 641104, India
J Ther Eng 2024; 10(3): 657-669 DOI: 10.14744/thermal.0000817
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Abstract

The assessment of performance is the key role factor for the gearboxes in the field of wind turbine industry. The thermal performance depends upon the viscous forces of the oil; bearing with stand capacity of the gearboxes and unnecessary irrotational forces or movements caused during the rotation of the gears at intermediate stage and high speed stage. The generation of the power starts from 15 m/s to 25 m/s with the starting rpm of 15 rpm to 1150 rpm; from initial stage to high speed stage of the gearbox. Hence the reduction of torque at higher revolutions may tends to complete reduction in power; owing to the thermal performance drop occurred due to the reduction of oil viscosities; improper maintenance during the high load conditions. This may lead to cause higher maintenance costs for the investors who is coming in front to invest huge amount of money. This present experimental work deals with latest sensors utilization to analyse the data from master gear box to slave gear box. From the results it is observed that the implementation of latest technology sensors tends to improves the maintanence costs by 20% as compared to conventional sensors. Hence it is adviced to implement the latest technology sensors which is capable to measure the wind speed loads of 20 m/s to 45 m/s. This gives range of resolution for downloading the past data and predicting the futurized data for evaluating the thermal performance drop; leads to save the maintanence costs 20% as compared to conventional methods.