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Parameter Identification of Optimized Fractional Maximum Power Point Tracking for Thermoelectric Generation Systems Using Manta Ray Foraging Optimization

Author

Listed:
  • Ahmed Fathy

    (Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Hegazy Rezk

    (Electrical Engineering Department, College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Dalia Yousri

    (Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt)

  • Essam H. Houssein

    (Faculty of Computers and Information, Minia University, Minia 61519, Egypt)

  • Rania M. Ghoniem

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia
    Department of Computer, Mansoura University, Mansoura 35516, Egypt)

Abstract

Thermoelectric generation systems (TEGSs) are used to convert temperature difference and heat flow into DC power based on the Seebeck theorem. The basic unit of TEGS is the thermoelectric module (TEM). TEGSs have gained increasing interest in the research fields of sustainable energy. The output power from TEM is mostly reliant on differential temperature between the hot and cold sides of the TEM added to the value of the load. As such, a robust MPPT strategy (MPPTS) is required to ensure that the TEGS is operating near to the MPP while varying the operating conditions. Two main drawbacks may occur in the conventional MPPTSs: low dynamic response, such as in the incremental resistance (INR) method, and oscillations around MPP at steady state, such as in the hill climbing (HC) method. In the current research work, an optimized fractional MPPTS is developed to improve the tracking performance of the TEGS, and remove the two drawbacks of the conventional MPPTSs. The proposed strategy is based on fractional order control (FOC). The main advantage of FOC is that it offers extra flexible time and frequency responses of the control system consent for better and robust performance. The optimal parameters of the optimized fractional MPPTS are identified by a manta ray foraging optimization (MRFO). To verify the robustness of the MRFO, the obtained results are compared with ten other algorithms: particle swarm optimization; whale optimization algorithm; Harris hawks optimization; heap-based optimizer; gradient-based optimizer; grey wolf optimizer; slime mould algorithm; genetic algorithm; seagull optimization algorithm (SOA); and tunicate swarm algorithm. The maximum average cost function of 4.92934 kWh has been achieved by MRFO, followed by SOA (4.5721 kWh). The lowest STD of 0.04867 was also accomplished by MRFO. The maximum efficiency of 99.46% has been obtained by MRFO, whereas the lowest efficiency of 74.01% was obtained by GA. Finally, the main findings proved the superiority of optimized fractional MPPTS compared with conventional methods for both steady-state and dynamic responses.

Suggested Citation

  • Ahmed Fathy & Hegazy Rezk & Dalia Yousri & Essam H. Houssein & Rania M. Ghoniem, 2021. "Parameter Identification of Optimized Fractional Maximum Power Point Tracking for Thermoelectric Generation Systems Using Manta Ray Foraging Optimization," Mathematics, MDPI, vol. 9(22), pages 1-18, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2971-:d:684291
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    References listed on IDEAS

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    1. Compadre Torrecilla, Marcos & Montecucco, Andrea & Siviter, Jonathan & Knox, Andrew R. & Strain, Andrew, 2019. "Novel model and maximum power tracking algorithm for thermoelectric generators operated under constant heat flux," Applied Energy, Elsevier, vol. 256(C).
    2. Torrecilla, Marcos Compadre & Montecucco, Andrea & Siviter, Jonathan & Strain, Andrew & Knox, Andrew R., 2018. "Transient response of a thermoelectric generator to load steps under constant heat flux," Applied Energy, Elsevier, vol. 212(C), pages 293-303.
    3. N. Kanagaraj & Hegazy Rezk & Mohamed R. Gomaa, 2020. "A Variable Fractional Order Fuzzy Logic Control Based MPPT Technique for Improving Energy Conversion Efficiency of Thermoelectric Power Generator," Energies, MDPI, vol. 13(17), pages 1-18, September.
    4. Verma, Vishal & Kane, Aarti & Singh, Bhim, 2016. "Complementary performance enhancement of PV energy system through thermoelectric generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1017-1026.
    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.
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    Cited by:

    1. Carlos Andres Ramos-Paja & Oscar Danilo Montoya & Luis Fernando Grisales-Noreña, 2022. "Adaptive Sliding-Mode Controller for Flyback-Based PV Systems Featuring Constant Switching Frequency," Mathematics, MDPI, vol. 10(8), pages 1-27, April.
    2. Hegazy Rezk & Abdul Ghani Olabi & Rania M. Ghoniem & Mohammad Ali Abdelkareem, 2023. "Optimized Fractional Maximum Power Point Tracking Using Bald Eagle Search for Thermoelectric Generation System," Energies, MDPI, vol. 16(10), pages 1-15, May.
    3. Alma Y. Alanis, 2022. "Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications," Mathematics, MDPI, vol. 10(13), pages 1-2, July.

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