Condition monitoring and performance forecasting of wind turbines based on denoising autoencoder and novel convolutional neural networks

With the proportion of wind power in the grid increasing, the monitoring and maintenance of wind turbines is becoming more and more important for the reliability of the grid. In this study, a data-driven modelling framework based on deep convolutional neural networks is constructed for wind turbines...

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Vydané v:Energy reports Ročník 7; s. 6354 - 6365
Hlavní autori: Jia, Xiongjie, Han, Yang, Li, Yanjun, Sang, Yichen, Zhang, Guolei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.11.2021
Elsevier
Predmet:
Condition monitoring
Denoising autoencoder
Performance forecasting
Residual attention module
Wind turbine
Condition monitoring
Performance forecasting
Denoising autoencoder
Residual attention module
Wind turbine
ISSN:2352-4847, 2352-4847
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Popis
Shrnutí:With the proportion of wind power in the grid increasing, the monitoring and maintenance of wind turbines is becoming more and more important for the reliability of the grid. In this study, a data-driven modelling framework based on deep convolutional neural networks is constructed for wind turbines condition monitoring (CM) and performance forecasting (PF). For CM, a robust denoising autoencoder (DAE) model is introduced to output the reconstruction error (RE) of raw signals. The RE is processed to a state indicator by exponentially weighted moving average (EWMA) and monitored on a control chart. For PF, two multi-steps ahead forecasting models are constructed for the forecasting of generator bearing and transformer temperature. To prevent overfitting caused by abundant features, the marginal effect analysis based on random forests is implemented to measure the importance of features. Besides, novel residual attention module (RAM) and training strategies are used improve their representation power of DAE and PF models. Experiments on a real wind turbine dataset prove the effectiveness of the proposed models and methods.
ISSN:2352-4847
2352-4847
DOI:10.1016/j.egyr.2021.09.080