Abstract
A sample of healthy wind turbines from the same wind farm with identical sizes and designs was investigated to determine the average vibrational signatures of the drive train components during normal operation. The units were variable-speed machines with three blades. The rotor was supported by two bearings, and the drive train connected to an intermediate three-stage planetary/helical gearbox. The nominal 2 MW output power was regulated using blade pitch adjustment. Vibrations were measured in exactly the same positions using the same type of sensors over a six-month period covering the entire range of operating conditions. The data set was preliminary validated to remove outliers based on the theoretical power curves. The most relevant frequency peaks in the rotor, gearbox, and generator vibrations were detected and identified based on averaged power spectra. The amplitudes of the peaks induced by a common source of excitation were compared in different measurement positions. A wind speed dependency of broadband vibration amplitudes was also observed. Finally, a fault detection case is presented showing the change of vibration signature induced by a damage in the gearbox.
Highlights
The rapid development and growth of the wind energy industry has resulted in the installation of a significant fleet of onshore and offshore wind turbines across the world
The rotor was supported by two bearings, and the drive train connected to an intermediate three-stage planetary/helical gearbox
From the spectra of all wind turbines (WT) with normalized frequency axes, the central amplitude percentile, has been plotted in black combined with a grey area denoting the values that lie between value, or the 50th percentile, has been plotted in black combined with a grey area denoting the values thethat
Summary
The rapid development and growth of the wind energy industry has resulted in the installation of a significant fleet of onshore and offshore wind turbines across the world. Proper operation and maintenance (O&M) is critical for maximizing the returns on the wind investments and for optimizing the total cost of ownership (TCO). This strategy seeks to minimize the production costs per unit of energy generated and to improve the turbine performance [1]. In this sense, condition monitoring (CM) systems have been developed to detect anomalies with the goal of minimizing machine downtime and maximizing availability. The early detection of faults is crucial for performing predictive (condition-based) maintenance on units in a wind farm [2,3,4,5,6]
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