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Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting

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  • Yao, Ganzhou
  • Luo, Zirong
  • Lu, Zhongyue
  • Wang, Mangkuan
  • Shang, Jianzhong
  • Guerrerob, Josep M.

Abstract

This comprehensive review systematically investigates diverse Maximum Power Point Tracking (MPPT) control strategies in Point Absorber Wave Energy Conversion (PA-WEC) systems. It elucidates each technique's key characteristics, advantages, and limitations, along with their scope and principle, providing a crucial roadmap for future research in renewable and sustainable energy. A highlight is the proposition of an innovative hybrid MPPT method combining a wide-range input LLC resonant converter with an Advanced Particle Swarm Optimization (APSO) strategy, optimizing energy extraction under various wave conditions. The review evaluates existing MPPT techniques for PA-WEC systems, dissecting their efficiency, tracking speed, cost, complexity, and robustness. By utilizing the Improved Analytic Hierarchy Process (IAHP) entropy weighting method, the study facilitates a detailed analysis of performance metrics, recent advancements, and eighteen diverse MPPT strategies. Multi-index case study outcomes from the IAHP entropy weight method, supplemented with MATLAB simulations, vouch for the impressive comprehensive performance of the proposed hybrid MPPT method. By employing a mix of qualitative and quantitative methodologies for a rigorous analysis of the algorithmic principles integral to wave energy MPPT and an examination of their practical adaptability. In essence, this review serves as a valuable compass for industry professionals, policymakers, and researchers aiming to advance the field of wave energy conversion. It emphasizes the potential of pioneering MPPT control strategies to enhance the efficiency of PA-WEC systems, thereby providing a solid foundation for the pursuit of sustainable energy solutions.

Suggested Citation

  • Yao, Ganzhou & Luo, Zirong & Lu, Zhongyue & Wang, Mangkuan & Shang, Jianzhong & Guerrerob, Josep M., 2023. "Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    • Handle: RePEc:eee:rensus:v:185:y:2023:i:c:s1364032123004562
    DOI: 10.1016/j.rser.2023.113599
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    1. Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
    2. Messalti, Sabir & Harrag, Abdelghani & Loukriz, Abdelhamid, 2017. "A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 221-233.
    3. Gao, Yuping & Shao, Shuangquan & Zou, Huiming & Tang, Mingsheng & Xu, Hongbo & Tian, Changqing, 2016. "A fully floating system for a wave energy converter with direct-driven linear generator," Energy, Elsevier, vol. 95(C), pages 99-109.
    4. Yilmaz, Unal & Kircay, Ali & Borekci, Selim, 2018. "PV system fuzzy logic MPPT method and PI control as a charge controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 994-1001.
    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.
    6. Agamloh, Emmanuel B. & Wallace, Alan K. & von Jouanne, Annette, 2008. "A novel direct-drive ocean wave energy extraction concept with contact-less force transmission system," Renewable Energy, Elsevier, vol. 33(3), pages 520-529.
    7. Reza Reisi, Ali & Hassan Moradi, Mohammad & Jamasb, Shahriar, 2013. "Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 433-443.
    8. Younesian, Davood & Alam, Mohammad-Reza, 2017. "Multi-stable mechanisms for high-efficiency and broadband ocean wave energy harvesting," Applied Energy, Elsevier, vol. 197(C), pages 292-302.
    9. Ahmed, Jubaer & Salam, Zainal, 2015. "An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency," Applied Energy, Elsevier, vol. 150(C), pages 97-108.
    10. Houssamo, Issam & Locment, Fabrice & Sechilariu, Manuela, 2010. "Maximum power tracking for photovoltaic power system: Development and experimental comparison of two algorithms," Renewable Energy, Elsevier, vol. 35(10), pages 2381-2387.
    11. Lin, Yonggang & Bao, Jingwei & Liu, Hongwei & Li, Wei & Tu, Le & Zhang, Dahai, 2015. "Review of hydraulic transmission technologies for wave power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 194-203.
    12. Tunde Aderinto & Hua Li, 2019. "Review on Power Performance and Efficiency of Wave Energy Converters," Energies, MDPI, vol. 12(22), pages 1-24, November.
    13. Bigdeli, Nooshin, 2015. "Optimal management of hybrid PV/fuel cell/battery power system: A comparison of optimal hybrid approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 377-393.
    14. Saket, A. & Etemad-Shahidi, A., 2012. "Wave energy potential along the northern coasts of the Gulf of Oman, Iran," Renewable Energy, Elsevier, vol. 40(1), pages 90-97.
    15. Dong, Feng & Pan, Shangzhi & Gong, Jinwu & Cai, Yuanqi, 2023. "Maximum power point tracking control strategy based on frequency and amplitude control for the wave energy conversion system," Renewable Energy, Elsevier, vol. 215(C).
    16. López, Iraide & Andreu, Jon & Ceballos, Salvador & Martínez de Alegría, Iñigo & Kortabarria, Iñigo, 2013. "Review of wave energy technologies and the necessary power-equipment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 413-434.
    17. Murai, Motohiko & Li, Qiao & Funada, Junki, 2021. "Study on power generation of single Point Absorber Wave Energy Converters (PA-WECs) and arrays of PA-WECs," Renewable Energy, Elsevier, vol. 164(C), pages 1121-1132.
    18. Wang, Liguo & Isberg, Jan & Tedeschi, Elisabetta, 2018. "Review of control strategies for wave energy conversion systems and their validation: the wave-to-wire approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 366-379.
    19. Ram, J.Prasanth & Rajasekar, N. & Miyatake, Masafumi, 2017. "Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1138-1159.
    20. Pelc, Robin & Fujita, Rod M., 2002. "Renewable energy from the ocean," Marine Policy, Elsevier, vol. 26(6), pages 471-479, November.
    21. Harrag, Abdelghani & Messalti, Sabir, 2015. "Variable step size modified P&O MPPT algorithm using GA-based hybrid offline/online PID controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1247-1260.
    22. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    23. Hu, Lu & Xue, Fei & Qin, Zijian & Shi, Jiying & Qiao, Wen & Yang, Wenjing & Yang, Ting, 2019. "Sliding mode extremum seeking control based on improved invasive weed optimization for MPPT in wind energy conversion system," Applied Energy, Elsevier, vol. 248(C), pages 567-575.
    24. Kermadi, Mostefa & Berkouk, El Madjid, 2017. "Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 369-386.
    25. Boumaaraf, Houria & Talha, Abdelaziz & Bouhali, Omar, 2015. "A three-phase NPC grid-connected inverter for photovoltaic applications using neural network MPPT," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1171-1179.
    26. Punitha, K. & Devaraj, D. & Sakthivel, S., 2013. "Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions," Energy, Elsevier, vol. 62(C), pages 330-340.
    27. Zhang, Yongxing & Zhao, Yongjie & Sun, Wei & Li, Jiaxuan, 2021. "Ocean wave energy converters: Technical principle, device realization, and performance evaluation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    28. Li, Yang & Huang, Lei & Chen, Minshuo & Tan, Peiwen & Hu, Minqiang, 2023. "Maximum power point tracking control based on inertia force for underwater direct-drive wave energy converter," Renewable Energy, Elsevier, vol. 215(C).
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