Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold

Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators (WTGs), and the existing methods are insufficient to achieve accurate and rapid fault diagnosis of WTGs, and the operation and maintenance costs of WTGs are too high. To invent a new method for fast and acc...

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Vydané v:Sensors (Basel, Switzerland) Ročník 23; číslo 13; s. 6198
Hlavní autori: Tang, Zhanjun, Shi, Xiaobing, Zou, Huayu, Zhu, Yuting, Yang, Yushi, Zhang, Yajia, He, Jianfeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 06.07.2023
MDPI
Predmet:
Accuracy
adaptive threshold
Air-turbines
Algorithms
Alternative energy sources
Bayes Theorem
Decision trees
EDP
Electric Power Supplies
Fault diagnosis
Machine Learning
Methods
Portugal
Power plants
Renewable resources
stacking integration algorithm
Turbines
Wind power
WTG
Portugal
EDP
fault diagnosis
stacking integration algorithm
WTG
machine learning
adaptive threshold
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí:Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators (WTGs), and the existing methods are insufficient to achieve accurate and rapid fault diagnosis of WTGs, and the operation and maintenance costs of WTGs are too high. To invent a new method for fast and accurate fault diagnosis of WTGs, this study constructs a stacking integration model based on the machine learning algorithms light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and stochastic gradient descent regressor (SGDRegressor) using publicly available datasets from Energias De Portugal (EDP). This model is automatically tuned for hyperparameters during training using Bayesian tuning, and the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the model to determine its applicability and accuracy. The fitted residuals of the test set were calculated, the Pauta criterion (3σ) and the temporal sliding window were applied, and a final adaptive threshold method for accurate fault diagnosis and alarming was created. The model validation results show that the adaptive threshold method proposed in this study is better than the fixed threshold for diagnosis, and the alarm times for the GENERATOR fault type, GENERATOR_BEARING fault type, and TRANSFORMER fault type are 1.5 h, 5.8 h, and 3 h earlier, respectively.
Bibliografia:ObjectType-Article-1
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23136198