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Effects of power take-off parameters and harvester shape on wave energy extraction and output of a hydraulic conversion system

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  • Gao, Hong
  • Xiao, Jie

Abstract

Considering nonlinear hydrodynamic forces of a harvester in irregular waves and a nonlinear hydraulic power take-off system, a time-domain nonlinear motion model and nonlinear hydraulic power take-off models are established to investigate the harvester motion response, the hydraulic system dynamic performance, the power extraction, the motor output power and efficiency. Matlab/Simulink is used to establish and simulate the system models. The convolution identification of nonlinear radiation force is based on a state-space model. The parameters of the state-space model and the infinity frequency added mass are fitted based on the least square method. The nonlinear hydrostatic storing force is considered for cone and hemisphere. The Pierson–Moskowitz 2-parameter spectrum is adopted for the irregular waves. The Coulomb friction force and viscous friction force based on the pressure difference and hydraulic cylinder parameters are modeled in the power take-off force. Under a given sea state, for cone, cylinder and hemisphere, the optimal hydraulic power take-off parameters are predicted based on a genetic algorithm for the maximum motor output power. The effects of the hydraulic cylinder piston working area, the motor displacement, the high pressure accumulator pre-charge pressure and initial volume, the significant wave height and energy period, the harvester diameter, draft and shape on the extracted power, the extraction efficiency, the motor output power and the hydraulic conversion efficiency are investigated. The extraction ability of the cone harvester is the highest among three shapes under the same diameter and draft. The conversion efficiency increases as the motor displacement decreases.

Suggested Citation

  • Gao, Hong & Xiao, Jie, 2021. "Effects of power take-off parameters and harvester shape on wave energy extraction and output of a hydraulic conversion system," Applied Energy, Elsevier, vol. 299(C).
    • Handle: RePEc:eee:appene:v:299:y:2021:i:c:s0306261921006954
    DOI: 10.1016/j.apenergy.2021.117278
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    2. Gao, Ruobin & Li, Ruilin & Hu, Minghui & Suganthan, Ponnuthurai Nagaratnam & Yuen, Kum Fai, 2023. "Dynamic ensemble deep echo state network for significant wave height forecasting," Applied Energy, Elsevier, vol. 329(C).

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