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Physics-informed deep learning model in wind turbine response prediction

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  • Li, Xuan
  • Zhang, Wei

Abstract

Subjected to strong cyclic wind and wave loads, wind turbines could experience severe fatigue damages and possibly fail to function normally due to accumulated damages at certain critical locations. Therefore, fatigue damage evaluation and prediction are essential and important to be conducted, which could involve massive numerical simulations and computational costs due to dynamic analyses of the wind turbines under various environmental conditions. To reduce the calculation cost related to the time-consuming dynamic analysis, sequence models such as the recurrent neural network (RNN) and the long-short term memory model (LSTM) originated from the deep learning topic are good and promising candidates to predict structural dynamic responses at multiple critical locations under different environmental scenarios. However, the training cost and prediction accuracy of these deep learning models might not be satisfiable since these models are purely data-driven and require significant amount of training data and a large number of training parameters. To reduce the computational cost and improve the prediction accuracy, a hybrid method that integrates the physical information of the underlying wind turbine system into the data-driven model is implemented in the present study as a computationally efficient simulation model. Structural properties and linearized representations of the wind turbine system are served as the physical constraints and applied in a recently proposed deep residual recurrent neural network (DR-RNN) to form as a physics-informed deep learning model. This physics-informed model is first applied to a frame structure with four degrees of freedom as a benchmark study to show the accuracy and efficiency of this model. The applicability of this physics-informed model to a complex wind turbine system is then investigated, and the performance of the developed physics-informed model on the structural response prediction is also compared with a regular data-driven model.

Suggested Citation

  • Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
    • Handle: RePEc:eee:renene:v:185:y:2022:i:c:p:932-944
    DOI: 10.1016/j.renene.2021.12.058
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    References listed on IDEAS

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    1. Choe, Do-Eun & Kim, Hyoung-Chul & Kim, Moo-Hyun, 2021. "Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades," Renewable Energy, Elsevier, vol. 174(C), pages 218-235.
    2. Li, Xuan & Zhang, Wei, 2020. "Long-term assessment of a floating offshore wind turbine under environmental conditions with multivariate dependence structures," Renewable Energy, Elsevier, vol. 147(P1), pages 764-775.
    3. Li, Xuan & Zhang, Wei, 2020. "Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions," Renewable Energy, Elsevier, vol. 159(C), pages 570-584.
    4. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
    5. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
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    Cited by:

    1. Zhang, Bin & Hu, Weihao & Xu, Xiao & Li, Tao & Zhang, Zhenyuan & Chen, Zhe, 2022. "Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 433-448.
    2. Mohammad Barooni & Turaj Ashuri & Deniz Velioglu Sogut & Stephen Wood & Shiva Ghaderpour Taleghani, 2022. "Floating Offshore Wind Turbines: Current Status and Future Prospects," Energies, MDPI, vol. 16(1), pages 1-28, December.
    3. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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