scholarly journals Neural networks applications for fault detection on wind turbines

2011 ◽  
pp. 1138-1141 ◽  
Author(s):  
R.F. Mesquita Brandao ◽  
J.A. Beleza Carvalho ◽  
F.P. Maciel Barbosa
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Wind Turbines

scholarly journals Cross-Turbine Training of Convolutional Neural Networks for SCADA-Based Fault Detection in Wind Turbines

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Markus Ulmer ◽  
Eskil Jarlskog ◽  
Gianmarco Pizza ◽  
Lilach Goren Huber
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Convolutional Neural Networks ◽  
Wind Turbines ◽  
Practical Aspect ◽  
Machine Learning Algorithms ◽  
Training Scheme ◽  
Early Fault Detection ◽  
Training Schemes ◽  
Energy Community

Machine learning algorithms for early fault detection of wind turbines using 10-minute SCADA data are attracting attention in the wind energy community due to their cost-effectiveness. It has been recently shown that convolutional neural networks (CNNs) can significantly improve the performance of such algorithms. One practical aspect in the deployment of these algorithms is that they require a large amount of historical SCADA data for training. These are not always available, for example in the case of newly installed turbines. Here we suggest a cross-turbine training scheme for CNNs: we train a CNN model on a turbine with abundant data and use the trained network to detect faults in a different wind turbine for which only little data are available. We show that this scheme is able to detect faults with an accuracy and robustness which are very similar to the single-turbine scheme, in which training and detection are both done on the same turbine. We demonstrate this for two different fault types: abrupt and slowly evolving faults and perform a sensitivity analysis in order to compare the performance of the two training schemes. We show that the scheme works successfully also when training on turbines from another farm and with different measured variables than the target turbine.

Sign in / Sign up

Export Citation Format

Share Document