Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/6756
Title: Fault Detection of a Wind Turbine's Gearbox, based on Power Curve Modeling and an on-line Statistical Change Detection Algorithm
Authors: Shaheen, Basheer 
Abu Hanieh, Ahmed 
Németh, István 
Keywords: fault detection;;SCADA;Wind turbines - Monitoring;Machinery - Monitoring - Mathematical models;Change-point problems - Detection;Simulation;Optimization;Fault-tolerant computing
Issue Date: May-2021
Publisher: Acta Polytechnica Hugarica (Journal of Applied Sciences Hungary)
Source: Basheer Shaheen, Ahmed Abu Hanieh and István Németh, Fault detection of wind turbine's gearbox based on power curve modeling and an on-line statistical change detection algorithm, Acta Polytechnica Hugarica (Journal of Applied Sciences Hungary), Volume 18, Number 6, pp 175-196, May, 2021
Journal: Acta Polytechnica Hugarica (Journal of Applied Sciences Hungary) 
Abstract: An early model-based fault detection was developed, based on the wind turbine's power curve to detect the degradation (faults) in gearbox efficiency, resulted from the existing mechanical losses (torque losses) through the low-speed shaft and the high-speed shaft, then to assist in implementing predictive maintenance strategy. The detection was performed on two levels; the first level represents a slight and progressive degradation in the gearbox efficiency. The other one represents a radical (abrupt) degradation in the efficiency. Artificial SCADA data for different measurements (wind speed and active power) in both fault-free and faulty operating modes were generated using a FAST-NREL simulator. The wind turbine power curves' parameters were estimated, then power residuals were generated from each power point. Finally, an on-line CUSUM statistical change detection algorithm was used to evaluate and detect small changes in power residuals generated from the model. The presented fault detection system successfully detected faults in both detection levels under realistic wind turbulence and with a fault magnitude of 2% efficiency degradation for the progressive degradation level.
URI: http://hdl.handle.net/20.500.11889/6756
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