Recurrent neural network optimization for wind turbine condition prognosis
Faculty of Technology, Université 20 août 1955-Skikda, Algeria
LGMM Laboratory, Faculty of Technology, Université 20 août 1955-Skikda, Algeria
Kerboua Adlen   

Faculty of Technology, Université 20 août 1955-Skikda
Data nadesłania: 21-04-2022
Data ostatniej rewizji: 20-06-2022
Data akceptacji: 25-06-2022
Data publikacji online: 27-06-2022
Data publikacji: 27-06-2022
This research focuses on employing Recurrent Neural Networks (RNN) to prognosis a wind turbine operation’s health from collected vibration time series data, by using several memory cell variations, including Long Short Time Memory (LSTM), Bilateral LSTM (BiLSTM), and Gated Recurrent Unit (GRU), which are integrated into various architectures. We tune the training hyperparameters as well as the adapted depth and recurrent cell number of the proposed networks to obtain the most accurate predictions. Tuning those parameters is a hard task and depends widely on the experience of the designer. This can be resolved by integrating the training process in a Bayesian optimization loop where the loss is considered as the objective function to minimize. The obtained results show the effectiveness of the proposed method, which generates more accurate recurrent models with a more accurate prognosis of the operating state of the wind turbine than those generated using trivial training parameters.
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