scholarly journals The detection of wind turbine shaft misalignment using temperature monitoring

Author(s):  
Oliver Tonks ◽  
Qing Wang
Author(s):  
Boualem Merainani ◽  
Sofiane Laddada ◽  
Eric Bechhoefer ◽  
Mohamed Abdessamed Ait Chikh ◽  
Djamel Benazzouz

2021 ◽  
Vol 118 (3) ◽  
pp. 134-141
Author(s):  
Аlina Fazylova

Eddy current sensors are used to measure shaft clearance in wind turbines and to check that there is a thin film of oil in the clearance. In this case, the oil is usually applied under pressure. Because the eddy current sensors are resistant to oil, pressure and temperature, this allows them to operate reliably in these hostile environments. When the gap becomes too large, a maintenance warning is generated. Eddy current sensors help detect axial and radial deflection of the turbine shaft. Radial movement occurs when the shaft is off-center. Axial movement indicates that the shaft is tilted relative to the central axis. Both cannot be eliminated completely. However, with significant deviations, increased bearing wear occurs. If such situations are detected, the turbine should be shut down as soon as possible for maintenance, even before an accident occurs. Finally, eddy current sensors are used to measure forces or torques applied to the nacelle. These influences can be caused by vibration, wind loads or other factors that, over time, can lead to the destruction of the entire structure. Eddy current sensors can also be used to measure axial, radial or tangential deflection of clutch discs, which ensure the safety of the rotor in the event of strong winds. This article provides a method for calculating an inductive sensor. This calculation will allow you to correctly develop a wind turbine eddy current sensor.


Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1601
Author(s):  
Ahmed Al-Ajmi ◽  
Yingzhao Wang ◽  
Siniša Djurović

With a continued strong increase in wind generator applications, the condition monitoring of wind turbine systems has become ever more important in ensuring the availability and reduced cost of produced power. One of the key turbine conditions requiring constant monitoring is the generator shaft alignment, which if compromised and untreated can lead to catastrophic system failures. This study explores the possibility of employing supervised machine learning methods on the readily available generator controller loop signals to achieve detection of shaft misalignment condition. This could provide a highly noninvasive and low-cost solution for misalignment monitoring in comparison with the current misalignment monitoring field practice that relies on invasive and costly drivetrain vibration analysis. The study utilises signal datasets measured on a dedicated doubly fed induction generator test rig to demonstrate that high consistency and accuracy recognition of shaft angular misalignment can be achieved through the application of supervised machine learning on controller loop signals. The average recognition accuracy rate of up to 98.8% is shown to be attainable through analysis of a key feature subset of the stator flux-oriented controller signals in a range of operating speeds and loads.


2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
T. Magesh ◽  
C. Chellamuthu

Power quality issues associated with the fixed speed wind farm substation located at Coimbatore district are investigated as the wind generators are tripping frequently. The investigations are carried out using two power quality analyzers, Fluke 435 and Dranetz PX5.8, with one of them connected at group control breaker of the 110 kV feeder and the other at the selected 0.69 kV generator busbar during the period of maximum power generation. From the analysis of the recorded data it is found that sag, swell, and transients are the major events which are responsible for the tripping of the generators. In the present study, simulation models for wind, turbine, shaft, pitch mechanism, induction generator, and grid are developed using DIgSILENT. Using the turbine characteristics, a two-dimensional lookup table is designed to generate a reference pitch angle necessary to simulate the power curve of the passive stall controlled wind turbine. Various scenarios and their effects on the performance of the wind farm are studied and validated with the recorded data and waveforms. The simulation model will be useful for the designers for planning and development of the wind farm before implementation.


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