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Optimization of hydraulic power take-off system settings for point absorber wave energy converter

Author

Listed:
  • Amini, Erfan
  • Mehdipour, Hossein
  • Faraggiana, Emilio
  • Golbaz, Danial
  • Mozaffari, Sevda
  • Bracco, Giovanni
  • Neshat, Mehdi

Abstract

Ocean wave renewable energy is becoming a key part of the renewable energy industry over the recent decades. By developing wave energy converters (WECs), their power take-off (PTO) systems have been investigated to enhance the power extraction from the ocean. Adjusting PTO parameters is a challenging optimization problem because there is a complex and nonlinear relationship between these parameters and the absorbed power output. In this regard, this study aims to optimize the PTO system parameters of a point absorber wave energy converter in the wave data-set in Perth, on the Western Australian coasts. The converter is numerically designed to oscillate against irregular and multi-dimensional waves and sensitivity analysis for PTO settings. Then, to find the optimal PTO system parameters which lead to the highest power output, ten optimization approaches are incorporated to solve the nonlinear problem, including the Nelder-Mead search method, Active-set method, Sequential quadratic Programming method (SQP), Multi-Verse Optimizer (MVO), and six modified combination of Genetic, Surrogate and fminsearch techniques. After a feasibility landscape analysis, the optimization outcome is carried out and gives us the best answer in terms of PTO system settings. Finally, the investigation shows that the modified combinations of Genetic, Surrogate, and fminsearch approaches can outperform the others in the selected wave scenario, as well as with regard to the interaction between PTO system variables.

Suggested Citation

  • Amini, Erfan & Mehdipour, Hossein & Faraggiana, Emilio & Golbaz, Danial & Mozaffari, Sevda & Bracco, Giovanni & Neshat, Mehdi, 2022. "Optimization of hydraulic power take-off system settings for point absorber wave energy converter," Renewable Energy, Elsevier, vol. 194(C), pages 938-954.
    • Handle: RePEc:eee:renene:v:194:y:2022:i:c:p:938-954
    DOI: 10.1016/j.renene.2022.05.164
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    2. Fatemehsadat Mirshafiee & Emad Shahbazi & Mohadeseh Safi & Rituraj Rituraj, 2023. "Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study," Energies, MDPI, vol. 16(1), pages 1-20, January.

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