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A Real-Time Implementation of Novel and Stable Variable Step Size MPPT

Author

Listed:
  • Maissa Farhat

    (Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Sheikh Saqr Bin Khalid Rd, Ras al Khaimah 10021, UAE)

  • Oscar Barambones

    (Advanced Control Group, Universidad del País Vasco, EUI, Nieves Cano 12, 01006 Vitoria, Spain)

  • Lassaâd Sbita

    (Research Unit of Photovoltaic, Wind and Geothermal Systems, National Engineering School of Gabes, University of Gabes, Rue Omar Ibn-Elkhattab, Zrig, Gabes 900211, Tunisia)

Abstract

This paper presents a complete study of a standalone photovoltaic (PV) system including a maximum power tracker (MPPT) driving a DC boost converter to feed a resistive load. Here, a new MPPT approach using a modification on the original perturb and observe (P&O) algorithm is proposed; the improved algorithm is founded on a variable step size (VSZ). This novel algorithm is realized and efficiently implemented in the PV system. The proposed VSZ algorithm is compared both in simulation and in real time to the P&O algorithm. The stability analysis for the VSZ algorithm is performed using Lyapunov’s stability theory. In this paper, a detailed study and explanation of the modified P&O MPPT controller is presented to ensure high PV system performance. The proposed algorithm is practically implemented using a DSP1104 for real-time testing. Significant results are achieved, proving the validity of the proposed PV system control scheme. The obtained results show that the proposed VSZ succeeds at harvesting the maximum power point (MPP), as the amount of harvested power using VSZ is three times greater than the power extracted without the tracking algorithm. The VSZ reveals improved performance compared to the conventional P&O algorithm in term of dynamic response, signal quality and stability.

Suggested Citation

  • Maissa Farhat & Oscar Barambones & Lassaâd Sbita, 2020. "A Real-Time Implementation of Novel and Stable Variable Step Size MPPT," Energies, MDPI, vol. 13(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4668-:d:410396
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    References listed on IDEAS

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    1. Luigi Piegari & Renato Rizzo & Ivan Spina & Pietro Tricoli, 2015. "Optimized Adaptive Perturb and Observe Maximum Power Point Tracking Control for Photovoltaic Generation," Energies, MDPI, vol. 8(5), pages 1-19, April.
    2. Reza Reisi, Ali & Hassan Moradi, Mohammad & Jamasb, Shahriar, 2013. "Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 433-443.
    3. Sheik Mohammed, S. & Devaraj, D. & Imthias Ahamed, T.P., 2016. "A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV system," Energy, Elsevier, vol. 112(C), pages 1096-1106.
    4. Farhat, Maissa & Barambones, Oscar & Sbita, Lassaâd, 2015. "Efficiency optimization of a DSP-based standalone PV system using a stable single input fuzzy logic controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 907-920.
    5. Farhat, Maissa & Barambones, Oscar & Sbita, Lassaad, 2017. "A new maximum power point method based on a sliding mode approach for solar energy harvesting," Applied Energy, Elsevier, vol. 185(P2), pages 1185-1198.
    6. Zakariya M. Dalala & Osama Saadeh & Mathhar Bdour & Zaka Ullah Zahid, 2018. "A New Maximum Power Point Tracking (MPPT) Algorithm for Thermoelectric Generators with Reduced Voltage Sensors Count Control †," Energies, MDPI, vol. 11(7), pages 1-16, July.
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    4. Eneko Artetxe & Jokin Uralde & Oscar Barambones & Isidro Calvo & Imanol Martin, 2023. "Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin," Mathematics, MDPI, vol. 11(9), pages 1-21, May.
    5. Kostas Bavarinos & Anastasios Dounis & Panagiotis Kofinas, 2021. "Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms," Energies, MDPI, vol. 14(2), pages 1-23, January.
    6. Esteban Guerrero-Ramirez & Alberto Martinez-Barbosa & Marco Antonio Contreras-Ordaz & Gerardo Guerrero-Ramirez & Enrique Guzman-Ramirez & Jorge Luis Barahona-Avalos & Manuel Adam-Medina, 2022. "DC Motor Drive Powered by Solar Photovoltaic Energy: An FPGA-Based Active Disturbance Rejection Control Approach," Energies, MDPI, vol. 15(18), pages 1-36, September.

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    Keywords

    PV; MPPT; P& O; VSZ; dSPACE;
    All these keywords.

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