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Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review

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  • Ram, J.Prasanth
  • Rajasekar, N.
  • Miyatake, Masafumi

Abstract

Increased penetration of wind and solar PV system in Distributed Generation (DG) and isolated micro grid environment necessitates the use of maximum power point tracking method for wind and solar PV resources. Considering the change in environmental conditions and non-linearity, a variety of publications reporting various MPPT algorithms for solar and wind energy systems are put forward in recent times. But the review reports on common MPPT techniques used for solar and wind applications for hybrid power generation have not yet been reported. Hence, in this paper, conventional techniques and artificial intelligent techniques found extensively used in the power generation platform are peerly reviewed and compared. Historical MPPT methods like Perturb & Observe (P&O) / Hill Climbing, Incremental Conductance (INC), Fuzzy and Neural Network methods benchmarked in MPPT province are comprehensively compared in a common platform. In addition to the common existing techniques, recent swarm intelligence and bio-inspired techniques in solar PV and sensor less adaptive techniques in wind MPPT are also been reviewed provided for quality assessment. Finally an economic analysis is arrived for MPPT methods based on (i) Capacity utilization factor (ii) Cost (iii) Energy Savings (iv) payback period (v) Income generated and (vi) stability.

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  • 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.
    • Handle: RePEc:eee:rensus:v:73:y:2017:i:c:p:1138-1159
    DOI: 10.1016/j.rser.2017.02.009
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    5. Ram, J. Prasanth & Manghani, Himanshu & Pillai, Dhanup S. & Babu, T. Sudhakar & Miyatake, Masafumi & Rajasekar, N., 2018. "Analysis on solar PV emulators: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 149-160.
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    11. Prasanth Ram, J. & Rajasekar, N., 2017. "A new robust, mutated and fast tracking LPSO method for solar PV maximum power point tracking under partial shaded conditions," Applied Energy, Elsevier, vol. 201(C), pages 45-59.
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    13. Haoming Liu & Muhammad Yasir Ali Khan & Xiaoling Yuan, 2023. "Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review," Energies, MDPI, vol. 16(15), pages 1-64, July.
    14. Song, Dongran & Liu, Junbo & Yang, Yinggang & Yang, Jian & Su, Mei & Wang, Yun & Gui, Ning & Yang, Xuebing & Huang, Lingxiang & Hoon Joo, Young, 2021. "Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm," Energy, Elsevier, vol. 221(C).
    15. Sirine Rabah & Aida Zaier & Jaime Lloret & Hassen Dahman, 2023. "Efficiency Enhancement of a Hybrid Sustainable Energy Harvesting System Using HHHOPSO-MPPT for IoT Devices," Sustainability, MDPI, vol. 15(13), pages 1-28, June.

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