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Novel highly accurate universal maximum power point tracker for maximum power extraction from hybrid fuel cell/photovoltaic/wind power generation systems

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  • Fathabadi, Hassan

Abstract

All the maximum power point tracking (MPPT) units that are currently used in hybrid systems include several distinct MPPT controllers, so that, each MPPT controller is dedicated to a subsystem. Using a distinct MPPT algorithm and controller for each subsystem of a hybrid system explicitly complicates the system implementation, increases cost, and decreases the accuracy of the MPPT process. This paper addresses this problem by presenting a novel fast and highly accurate universal maximum power point (MPP) tracker for hybrid fuel cell/photovoltaic/wind power generation systems. The tracker is called “universal tracker” because it uses a unified algorithm and controller to concurrently track the MPPs of the photovoltaic (PV), fuel cell (FC) and wind energy conversion (WEC) subsystems of a hybrid FC/PV/wind power system. The proposed universal MPP tracker only uses the output voltages and currents of the PV module, FC stack, and WEC subsystem used in a hybrid power system, i.e., it does not need any expensive sensors such as anemometers and tachometers. Moreover, the technique tracks the MPP of the WEC subsystem, not the MPP of its wind turbine, so it extracts the highest output electrical power from the WEC subsystem. A hybrid FC/PV/wind power generation system has been built to validate theoretical results and evaluate the tracker performances. It is experimentally verified that the universal MPP tracker performs a very fast and highly accurate MPPT process, so that, the MPPT efficiencies are about 99.60%, 99.41%, and 99.28% respectively in the PV, FC and WEC subsystems with the very short tracking convergence times of 12 ms, 33 s, and 25 s, respectively. A comparison between the tracker and the state-of-the-art MPP trackers has been also performed that explicitly demonstrates the better performances of the proposed universal MPP tracker, while it concurrently tracks three MPPs but others track only one MPP.

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  • Fathabadi, Hassan, 2016. "Novel highly accurate universal maximum power point tracker for maximum power extraction from hybrid fuel cell/photovoltaic/wind power generation systems," Energy, Elsevier, vol. 116(P1), pages 402-416.
    • Handle: RePEc:eee:energy:v:116:y:2016:i:p1:p:402-416
    DOI: 10.1016/j.energy.2016.09.095
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    References listed on IDEAS

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    2. Büyük, Mehmet & İnci, Mustafa, 2023. "Improved drift-free P&O MPPT method to enhance energy harvesting capability for dynamic operating conditions of fuel cells," Energy, Elsevier, vol. 267(C).
    3. Kebir, Anouer & Woodward, Lyne & Akhrif, Ouassima, 2019. "Real-time optimization of renewable energy sources power using neural network-based anticipative extremum-seeking control," Renewable Energy, Elsevier, vol. 134(C), pages 914-926.
    4. Rezk, Hegazy & Fathy, Ahmed & Abdelaziz, Almoataz Y., 2017. "A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 377-386.
    5. Mao, Mingxuan & Zhang, Li & Duan, Pan & Duan, Qichang & Yang, Ming, 2018. "Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller," Energy, Elsevier, vol. 143(C), pages 181-190.
    6. Mohammad Junaid Khan & Divesh Kumar & Yogendra Narayan & Hasmat Malik & Fausto Pedro García Márquez & Carlos Quiterio Gómez Muñoz, 2022. "A Novel Artificial Intelligence Maximum Power Point Tracking Technique for Integrated PV-WT-FC Frameworks," Energies, MDPI, vol. 15(9), pages 1-35, May.

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