Design and development of a wind turbine test rig for condition monitoring studies

Sailendu Biswal, G. Sabareesh
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引用次数: 30

Abstract

Wind energy is an emerging, clean and renewable source of energy. It is estimated that by year 2035, wind energy will be generating more than 25% of the world's electricity according to International Energy Agency (IEA). With the increase in demand for wind energy, its maintenance issues are becoming more prominent. The scheduled maintenance is more economical than unscheduled repair resulting from failure. So a continuous condition monitoring of various critical components like bearings, gearbox, and shafts of wind turbine is essential in order to enable predictive maintenance. 10% of the total failure is contributed by the bearings, shaft and gear box failures, but the downtime is more than 50% of the total downtime. This paper discusses the development of a bench-top test rig which is designed to mimic the operating condition of an actual wind turbine and use it for monitoring its condition so as to diagnose the incipient faults in its critical components using latest machine learning algorithms such as Artificial Neural Network (ANN).
风力机状态监测试验台的设计与研制
风能是一种新兴的、清洁的、可再生的能源。据国际能源署(IEA)估计,到2035年,风能将产生超过25%的世界电力。随着对风能需求的增加,其维护问题日益突出。定期维修比因故障而不定期维修更经济。因此,为了实现预测性维护,对风力涡轮机的各种关键部件(如轴承、齿轮箱和轴)进行持续状态监测是必不可少的。总故障的10%是由轴承、轴和齿轮箱故障贡献的,但停机时间占总停机时间的50%以上。本文讨论了一种台式试验台的开发,该试验台设计用于模拟实际风力发电机的运行状况,并使用最新的机器学习算法(如人工神经网络(ANN))来监测其状态,从而诊断其关键部件的早期故障。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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