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

Mingzhu Tang, Yutao Chen, Huawei Wu, Qi Zhao, Wen Long, Victor S. Sheng, Jiabiao Yi

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Article number686616
JournalFrontiers in Energy Research
StatePublished - May 25 2021


  • class imbalance
  • cost-sensitive learning
  • extremely randomized trees
  • fault detection
  • fault diagnosis
  • wind turbine generator


Dive into the research topics of 'Cost-Sensitive Extremely Randomized Trees Algorithm for Online Fault Detection of Wind Turbine Generators'. Together they form a unique fingerprint.

Cite this