Wind turbine fault detection and classification by means of image texture analysis

The future of the wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly offshore to benefit stronger and more uniform wind conditions. The cost of operation and maintenance of offshore wind turbines is approximately 15–35% o...

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Bibliographic Details
Published in:Mechanical systems and signal processing 2018-07, Vol.107, p.149-167
Main Authors: Ruiz, Magda, Mujica, Luis E., Alférez, Santiago, Acho, Leonardo, Tutivén, Christian, Vidal, Yolanda, Rodellar, José, Pozo, Francesc
Format: Article
Language:English
Subjects:
Computer simulation
Digital imaging
Fault classification
Fault detection
faultclassification
faultdetection
Feature recognition
Image classification
Image detection
Matemàtiques i estadística
Object recognition
Pattern recognition
Studies
Texture analysis
Texture recognition
textureanalysis
Turbines
Wavelet analysis
Wind power
Wind turbine
Wind turbines
windturbine
Àrees temàtiques de la UPC
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Summary:The future of the wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly offshore to benefit stronger and more uniform wind conditions. The cost of operation and maintenance of offshore wind turbines is approximately 15–35% of the total cost. Of this, 80% goes towards unplanned maintenance issues due to different faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and challenges is by applying low-cost advanced fault detection schemes. This work proposes a new method for detection and classification of wind turbine actuators and sensors faults in variable-speed wind turbines. For this purpose, time domain signals acquired from the operating wind turbine are represented as two-dimensional matrices to obtain grayscale digital images. Then, the image pattern recognition is processed getting texture features under a multichannel representation. In this work, four types of texture characteristics are used: statistical, wavelet, granulometric and Gabor features. Next, the most significant ones are selected using the conditional mutual criterion. Finally, the faults are detected and distinguished between them (classified) using an automatic classification tool. In particular, a 10-fold cross-validation is used to obtain a more generalized model and evaluates the classification performance. Coupled non-linear aero-hydro-servo-elastic simulations of a 5 MW offshore type wind turbine are carried out in several fault scenarios. The results show a promising methodology able to detect and classify the most common wind turbine faults.
ISSN:0888-3270
1096-1216
DOI:10.1016/j.ymssp.2017.12.035