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Optimal Parameter Identification of Single-Sensor Fractional Maximum Power Point Tracker for Thermoelectric Generator

Author

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  • Abdul Ghani Olabi

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Mechanical Engineering and Design, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK)

  • Hegazy Rezk

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt)

  • Enas Taha Sayed

    (Chemical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt)

  • Tabbi Awotwe

    (Mechanical Engineering and Design, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK)

  • Samah Ibrahim Alshathri

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Mohammad Ali Abdelkareem

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt)

Abstract

A thermoelectric generator (TEG) is used for converting temperature difference and into DC directly to electric energy based on the Seebeck effect. This new technology has attracted researchers of sustainable energy. The energy obtained from the TEG depends on the temperature difference between the two sides of the TEG. A reliable MPP “maximum power point” tracker (MPPT) is mandatory to guarantee that the TEG is working close to the MPP under different operational conditions. There are two common methods that have been widely used to track the MPP: hill climbing (HC) and incremental conductance (INR). The HC method is very fast in tracking the MPP; however, oscillation can occur under a high steady state. On the contrary, the INR method needs more time to track the MPP but does not oscillate around the MPP. To overcome these issues, fractional control is adopted. Furthermore, the proposed MPPT requires only a single current sensor, as opposed to conventional MPPTs, which require at least two sensors: current and voltage sensors. The cost of the control system is reduced when the number of sensors is reduced. Hunger games search optimization is used to estimate the parameters of a single sensor optimized fractional MPPT (OFMPPT). During the optimization process, three parameters were assigned as decision variables: proportional gain, integral gain, and order, with the objective function being the TEG’s energy. The results demonstrated the superiority of OFMPPT in both transient and steady state compared to HC and INR.

Suggested Citation

  • Abdul Ghani Olabi & Hegazy Rezk & Enas Taha Sayed & Tabbi Awotwe & Samah Ibrahim Alshathri & Mohammad Ali Abdelkareem, 2023. "Optimal Parameter Identification of Single-Sensor Fractional Maximum Power Point Tracker for Thermoelectric Generator," Sustainability, MDPI, vol. 15(6), pages 1-13, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5054-:d:1095590
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    References listed on IDEAS

    as
    1. He, Wei & Zhang, Gan & Zhang, Xingxing & Ji, Jie & Li, Guiqiang & Zhao, Xudong, 2015. "Recent development and application of thermoelectric generator and cooler," Applied Energy, Elsevier, vol. 143(C), pages 1-25.
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    1. 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.

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