期刊
ENERGY REPORTS
卷 7, 期 -, 页码 6354-6365出版社
ELSEVIER
DOI: 10.1016/j.egyr.2021.09.080
关键词
Wind turbine; Condition monitoring; Performance forecasting; Denoising autoencoder; Residual attention module
With the increasing proportion of wind power in the grid, the monitoring and maintenance of wind turbines are becoming more important. This study presents a data-driven modelling framework based on deep learning for effective monitoring of wind turbines and prediction of performance, which is validated through experiments.
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. (C) 2021 The Authors. Published by Elsevier Ltd.
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