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Development of a constraint non-causal wave energy control algorithm based on artificial intelligence

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  • Li, L.
  • Gao, Y.
  • Ning, D.Z.
  • Yuan, Z.M.

Abstract

The real-time implementation of wave energy control leads to non-causality as the wave load that comes in the next few seconds is used to optimize the control command. The present work tackles non-causality through online forecasting of future wave force using artificial intelligence technique. The past free surface elevation is used to forecast the incoming wave load. A feedforward artificial neural network is developed for the forecasting, which learns to establish the intrinsic link between past free surface elevation and future wave force through machine learning algorithm. With the implementation of the developed online wave force prediction algorithm, a real-time discrete control algorithm taking constraint on response amplitude into account is developed and implemented to a bi-oscillator wave energy converter in the present research. The dynamic response and the wave power extraction are simulated using a state-space hydrodynamic model. It is shown that the developed real-time control algorithm enhances the power capture substantially whereas the motion of the system is hardly increased. The prediction error effect on power extraction is investigated. The reduction of power extraction is mainly caused by phase error, whilst the amplitude error has minimal influence. A link between the power capture efficiency and the constraint on control is also identified.

Suggested Citation

  • Li, L. & Gao, Y. & Ning, D.Z. & Yuan, Z.M., 2021. "Development of a constraint non-causal wave energy control algorithm based on artificial intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:rensus:v:138:y:2021:i:c:s1364032120308042
    DOI: 10.1016/j.rser.2020.110519
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    References listed on IDEAS

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    Cited by:

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    2. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
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    6. Seyed Milad Mousavi & Majid Ghasemi & Mahsa Dehghan Manshadi & Amir Mosavi, 2021. "Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory," Mathematics, MDPI, vol. 9(8), pages 1-16, April.

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