Using maximum correlated kurtosis deconvolution method in the bearing fault detection of wind turbine generators

2017 ◽  
Author(s):  
Lorand Szabo
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Deconvolution Method ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Turbine Generators ◽  
Wind Turbine Generators

scholarly journals Cost-Sensitive Extremely Randomized Trees Algorithm for Online Fault Detection of Wind Turbine Generators

2021 ◽  
Vol 9 ◽  
Author(s):  
Mingzhu Tang ◽  
Yutao Chen ◽  
Huawei Wu ◽  
Qi Zhao ◽  
Wen Long ◽  
...  
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Feature Selection Method ◽  
Misclassification Cost ◽  
Imbalanced Classification ◽  
Extremely Randomized Trees ◽  
Turbine Generators ◽  
Wind Turbine Generators ◽  
Doubly Fed ◽  
The Cost

The number of normal samples of wind turbine generators is much larger than the number of fault samples. To solve the problem of imbalanced classification in wind turbine generator fault detection, a cost-sensitive extremely randomized trees (CS-ERT) algorithm is proposed in this paper, in which the cost-sensitive learning method is introduced into an extremely randomized trees (ERT) algorithm. Based on the classification misclassification cost and class distribution, the misclassification cost gain (MCG) is proposed as the score measure of the CS-ERT model growth process to improve the classification accuracy of minority classes. The Hilbert-Schmidt independence criterion lasso (HSICLasso) feature selection method is used to select strongly correlated non-redundant features of doubly-fed wind turbine generators. The effectiveness of the method was verified by experiments on four different failure datasets of wind turbine generators. The experiment results show that average missing detection rate, average misclassification cost and gMean of the improved algorithm better than those of the ERT algorithm. In addition, compared with the CSForest, AdaCost and MetaCost methods, the proposed method has better real-time fault detection performance.

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