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Predicting absorbed power of a wave energy converter in a nonlinear mixed sea

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  • Wang, Yingguang

Abstract

In this paper the power performances of a point absorber wave energy converter (WEC) operating in a nonlinear multi-directional mixed sea have been rigorously investigated. The absorbed power of the WEC Power Take Off (PTO) system has been calculated by incorporating a second order irregular wave model into a nonlinear dynamic filter. This is a new methodology that is uniquely proposed to the ocean wave energy research community. The predicted results of the WEC power performances have been rigorously analyzed and systematically compared, and the advantages of using our proposed new methodology under extreme wave states have been convincingly substantiated. The research findings in this paper highlight that it is important to employ a nonlinear wave theory under extreme wave states for studying the power performances of wave energy converters, but that linear wave theory gives good results under normal wave states.

Suggested Citation

  • Wang, Yingguang, 2020. "Predicting absorbed power of a wave energy converter in a nonlinear mixed sea," Renewable Energy, Elsevier, vol. 153(C), pages 362-374.
    • Handle: RePEc:eee:renene:v:153:y:2020:i:c:p:362-374
    DOI: 10.1016/j.renene.2020.02.031
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    References listed on IDEAS

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    1. Wang, Yingguang & Wang, Lifu, 2018. "Towards realistically predicting the power outputs of wave energy converters: Nonlinear simulation," Energy, Elsevier, vol. 144(C), pages 120-128.
    2. Wang, Yingguang, 2019. "Comparison of a Lagrangian and a Gaussian model for power output predictions in a random sea," Renewable Energy, Elsevier, vol. 134(C), pages 426-435.
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    Cited by:

    1. Rasool, Safdar & Muttaqi, Kashem M. & Sutanto, Danny, 2020. "Modelling of a wave-to-wire system for a wave farm and its response analysis against power quality and grid codes," Renewable Energy, Elsevier, vol. 162(C), pages 2041-2055.
    2. Wang, Yingguang, 2020. "A novel environmental contour method for predicting long-term extreme wave conditions," Renewable Energy, Elsevier, vol. 162(C), pages 926-933.

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