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Hybrid Moth-Flame Optimization Algorithm and Incremental Conductance for Tracking Maximum Power of Solar PV/Thermoelectric System under Different Conditions

Author

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  • Hegazy Rezk

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al Kharj 11991, Saudi Arabia
    Electrical Engineering Dept., Faculty of Engineering, Minia University, Minya 61519, Egypt)

  • Ziad Mohammed Ali

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al Kharj 11991, Saudi Arabia
    Electrical Engineering Dept., Faculty of Engineering, Aswan University, Aswan Governorate 81528, Egypt)

  • Omer Abdalla

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al Kharj 11991, Saudi Arabia
    University of Medical Sciences & Technology, Khartoum 11111, Sudan)

  • Obai Younis

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al Kharj 11991, Saudi Arabia
    Mechanical Engineering Department, Faculty of Engineering, University of Khartoum, Al Khurtum 11111, Sudan)

  • Mohamed Ramadan Gomaa

    (Mechanical Department, Faculty of Engineering, Mu’tah University, Al-Karak 61710, Jordan
    Mechanical Department, Benha Faculty of Engineering, Benha University, Benha 13512, Egypt)

  • Mauia Hashim

    (Sudan Academy of Science, Khartoum 11111, Sudan)

Abstract

For an efficient energy harvesting by the PV/thermoelectric system, the maximum power point tracking (MPPT) principle is targeted, aiming to operate the system close to peak power point. Under a uniform distribution of the solar irradiance, there is only one maximum power point (MPP), which easily can be efficiently determined by any traditional MPPT method, such as the incremental conductance (INC). A different situation will occur for the non-uniform distribution of solar irradiance, where more than one MPP will exist on the power versus voltage plot of the PV/thermoelectric system. The determination of the global MPP cannot be achieved by conventional methods. To deal with this issue the application of soft computing techniques based on optimization algorithms is used. However, MPPT based on optimization algorithms is very tedious and time consuming, especially under normal conditions. To solve this dilemma, this research examines a hybrid MPPT method, consisting of an incremental conductance (INC) approach and a moth-flame optimizer (MFO), referred to as (INC-MFO) procedure, to reach high adaptability at different environmental conditions. In this way, the combination of the two different algorithms facilitates the utilization of the advantages of the two methods, thereby resulting in a faster speed tracking with uniform radiation distribution and a high accuracy in the case of a non-uniform distribution. It is very important to mention that the INC method is used to track the maximum power point under normal conditions, whereas the MFO optimizer is most relevant for the global search under partial shading. The obtained results revealed that the proposed strategy performed best in both of the dynamic and the steady-state conditions at uniform and non-uniform radiation.

Suggested Citation

  • Hegazy Rezk & Ziad Mohammed Ali & Omer Abdalla & Obai Younis & Mohamed Ramadan Gomaa & Mauia Hashim, 2019. "Hybrid Moth-Flame Optimization Algorithm and Incremental Conductance for Tracking Maximum Power of Solar PV/Thermoelectric System under Different Conditions," Mathematics, MDPI, vol. 7(10), pages 1-21, September.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:10:p:875-:d:269202
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    References listed on IDEAS

    as
    1. Zhang, Jin & Xuan, Yimin & Yang, Lili, 2014. "Performance estimation of photovoltaic–thermoelectric hybrid systems," Energy, Elsevier, vol. 78(C), pages 895-903.
    2. 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.
    3. Zeb, K. & Ali, S.M. & Khan, B. & Mehmood, C.A. & Tareen, N. & Din, W. & Farid, U. & Haider, A., 2017. "A survey on waste heat recovery: Electric power generation and potential prospects within Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1142-1155.
    4. Marmoush, Mohamed M. & Rezk, Hegazy & Shehata, Nabila & Henry, Jean & Gomaa, Mohamed R., 2018. "A novel merging Tubular Daylight Device with Solar Water Heater – Experimental study," Renewable Energy, Elsevier, vol. 125(C), pages 947-961.
    5. Mohamed, Mohamed A. & Zaki Diab, Ahmed A. & Rezk, Hegazy, 2019. "Partial shading mitigation of PV systems via different meta-heuristic techniques," Renewable Energy, Elsevier, vol. 130(C), pages 1159-1175.
    6. Zhu, Wei & Deng, Yuan & Wang, Yao & Shen, Shengfei & Gulfam, Raza, 2016. "High-performance photovoltaic-thermoelectric hybrid power generation system with optimized thermal management," Energy, Elsevier, vol. 100(C), pages 91-101.
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    Cited by:

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    2. Haitham Alsaif & Shobhit K. Patel & Naim Ben Ali & Ammar Armghan & Khaled Aliqab, 2023. "Numerical Simulation and Structure Optimization of Multilayer Metamaterial Plus-Shaped Solar Absorber Design Based on Graphene and SiO 2 Substrate for Renewable Energy Generation," Mathematics, MDPI, vol. 11(2), pages 1-13, January.
    3. Fathy, Ahmed, 2023. "Efficient energy valley optimization approach for reconfiguring thermoelectric generator system under non-uniform heat distribution," Renewable Energy, Elsevier, vol. 217(C).
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    5. 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.

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