A novel control method to maximize the energy-harvesting capability of an adjustable slope angle wave energy converter


This paper proposes a novel energy maximization algorithm of a sliding-buoy wave energy converter (SBWEC).

An efficiency optimization mechanism is designed and integrated into the SBWEC.

The control logic is based on a learning vector quantitative neural network for classifying the wave information.

The effectiveness of the proposed approach is verified through both simulations and experiments.


This paper introduces a novel control approach to maximizing the output energy of an adjustable slope angle wave energy converter (ASAWEC) with oil-hydraulic power take-off. Different from typical floating-buoy WECs, the ASAWEC is capable of capturing wave energy from both heave and surge modes of wave motions. For different waves, online determination of the titling angle plays a significant role in optimizing the overall efficiency of the ASAWEC. To enhance this task, the proposed method was developed based on a learning vector quantitative neural network (LVQNN) algorithm. First, the LVQNN-based supervisor controller detects wave conditions and directly produces the optimal titling angles. Second, a so-called efficiency optimization mechanism (EOM) with a secondary controller was designed to regulate automatically the ASAWEC slope angle to the desired value sent from the supervisor controller. A prototype of the ASAWEC was fabricated and a series of simulations and experiments was performed to train the supervisor controller and validate the effectiveness of the proposed control approach with regular waves. The results indicated that the system could reach the optimal angle within 2s and subsequently, the output energy could be maximized. Compared to the performance of a system with a vertically fixed slope angle, an increase of 5% in the overall efficiency was achieved. In addition, simulations of the controlled system were performed with irregular waves to confirm the applicability of the proposed approach in practice.


  • Wave energy converter;
  • Oil-hydraulic power take-off;
  • Learning vector quantitative neural network;
  • Control systems

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