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Robust Fractional MPPT-Based Moth-Flame Optimization Algorithm for Thermoelectric Generation Applications

Author

Listed:
  • Hegazy Rezk

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Minia University, Minya 61519, Egypt)

  • Magdy M. Zaky

    (Engineering Department, Nuclear Research Center, Atomic Energy Authority (EAEA), Cairo 11787, Egypt)

  • Mohemmed Alhaider

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Mohamed A. Tolba

    (Reactors Department, Nuclear Research Center, Egyptian Atomic Energy Authority (EAEA), Cairo 11787, Egypt)

Abstract

Depending on the temperature difference between the hot and cold sides of the thermoelectric generator (TEG), the output performance of the TEG can be produced. This means that it is necessary to force a TEG based on robust maximum power point tracking (MPPT) to operate close to its MPP at any given temperature or load. In this paper, an improved fractional MPPT (IFMPPT) is proposed in order to increase the amount of energy that can be harvested from TEGs. According to the suggested method, fractional order control is used. A moth-flame optimizer (MFO) was used to determine IFMPPT’s optimal parameters. A comparison of the results obtained by the MFO is made with those obtained by a particle swarm optimizer, genetic algorithm, gray wolf optimizer, seagull optimization algorithm, and tunicate swarm algorithm in order to demonstrate MFO’s superiority. IFMPPT’s primary objective is to enhance dynamic responses and exclude steady-state oscillations. Consequently, incremental resistance and perturb and observe are compared with the proposed strategy’s performance. It was revealed that IFMPPT provides superior tracking results both in dynamic and steady-state conditions when compared with traditional methods.

Suggested Citation

  • Hegazy Rezk & Magdy M. Zaky & Mohemmed Alhaider & Mohamed A. Tolba, 2022. "Robust Fractional MPPT-Based Moth-Flame Optimization Algorithm for Thermoelectric Generation Applications," Energies, MDPI, vol. 15(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8836-:d:981482
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    References listed on IDEAS

    as
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