Abstract
Quantifying wind turbine (WT) gearbox fatigue life is a critical problem for preventive maintenance when unsolved. This paper proposes a practical approach that uses ten minutes’ average wind speed of Supervisory Control and Data Acquisition (SCADA) data to quantify a WT gearbox’s gear fatigue life. Wind turbulence impacts on gearbox fatigue are studied thoroughly. Short-term fatigue assessment for the gearbox is then performed using linear fatigue theory by considering WT responses under external and internal excitation. The results shows that for a three stage gearbox, the sun gear in the first stage and pinions in the 2nd and 3rd stage are the most vulnerable parts. High mean wind speed, especially above the rated range, leads to a high risk of gearbox fatigue damage. Increase of wind turbulence may not increase fatigue damage as long as a WT has an instant response to external excitation. An approach of using SCADA data recorded every ten minutes to quantify gearbox long-term damages is presented. The calculation results show that the approach effectively presents gears’ performance degradation by quantifying their fatigue damage. This is critical to improve WT reliability and meaningful for WT gearbox fatigue assessment theory. The result provides useful tools for future wind farm prognostic maintenance.
Highlights
The gearbox is an important mechanical component in a wind turbine (WT)
Models and approaches to calculate WT gearbox long-term fatigue damage are developed based on linear fatigue theory and wind speed statistical distribution [10,11]
The results show significant in short-term fatigue damage fatigue damage at different gearbox stages for thedifferences case of mean wind speeds above
Summary
The gearbox is an important mechanical component in a wind turbine (WT). Its availability and reliability attract attention due to the ultimate long downtime if it fails. Suitable methods and approaches are crucial for WT damage detection and fatigue accumulation estimation Related technologies such as machine fatigue damage prognosis would greatly increase the value of condition monitoring in the drive train of WTs [5]. WTs’ long-term fatigue life when they are operating under variable and turbulent wind conditions. Models and approaches to calculate WT gearbox long-term fatigue damage are developed based on linear fatigue theory and wind speed statistical distribution [10,11]. This paper is focused on the mechanism of a gearbox’s gear bending fatigue failure to develop a method to predict the fatigue life with practical supervisory control and data acquisition (SCADA). According to the low sampling rate characteristics of SCADA data, a practical approach is proposed to quantify long-term gearbox gear fatigue damage, which is derived from the theoretical calculations and analysis results
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