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Maximization of wave power extraction of a heave point absorber with a sea-state-based causal control algorithm

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  • Li, Liang
  • Gao, Zhen

Abstract

A causal control strategy is developed to tackle the non-causality arising in the real-time implementation of optimal wave energy control. The proposed control strategy utilizes a causal approximation transfer function to link the current wave force to the desired buoy velocity so as to eliminate the non-causality. An optimum model of the approximation transfer function is derived considering the power density distribution of the local wave spectrum. Based on the optimized approximation transfer function, the desired buoy velocity is achieved by tuning the power take-off mechanical force with the PID control. The efficiency of the proposed causal control strategy is assessed for a heaving point-absorber, in a set of random wave conditions. Generally, the heaving point-absorber could extract wave power up to 90% of the theoretical upper bound using the proposed control strategy. The sensitivity of the proposed control to viscous damping effect due to drag and wave spectrum bandwidth is investigated. Although it is less efficient in broad-banded sea state, the control efficiency is still within an acceptable level (above 70%).

Suggested Citation

  • Li, Liang & Gao, Zhen, 2020. "Maximization of wave power extraction of a heave point absorber with a sea-state-based causal control algorithm," Energy, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:energy:v:204:y:2020:i:c:s0360544220309889
    DOI: 10.1016/j.energy.2020.117881
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    References listed on IDEAS

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    1. Zhang, Xiantao & Tian, Xinliang & Xiao, Longfei & Li, Xin & Chen, Lifen, 2018. "Application of an adaptive bistable power capture mechanism to a point absorber wave energy converter," Applied Energy, Elsevier, vol. 228(C), pages 450-467.
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    4. Truong, D.Q. & Ahn, K.K., 2012. "Wave prediction based on a modified grey model MGM(1,1) for real-time control of wave energy converters in irregular waves," Renewable Energy, Elsevier, vol. 43(C), pages 242-255.
    5. Henriques, J.C.C. & Gato, L.M.C. & Falcão, A.F.O. & Robles, E. & Faÿ, F.-X., 2016. "Latching control of a floating oscillating-water-column wave energy converter," Renewable Energy, Elsevier, vol. 90(C), pages 229-241.
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    Cited by:

    1. 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).

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