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Metaheuristic Optimization of Fractional Order Incremental Conductance (FO-INC) Maximum Power Point Tracking (MPPT)

Author

Listed:
  • Hossam Hassan Ammar
  • Ahmad Taher Azar
  • Raafat Shalaby
  • M. I. Mahmoud

Abstract

This paper seeks to improve the photovoltaic (PV) system efficiency using metaheuristic, optimized fractional order incremental conductance (FO-INC) control. The proposed FO-INC controls the output voltage of the PV arrays to obtain maximum power point tracking (MPPT). Due to its simplicity and efficiency, the incremental conductance MPPT (INC-MPPT) is one of the most popular algorithms used in the PV scheme. However, owing to the nonlinearity and fractional order (FO) nature of both PV and DC-DC converters, the conventional INC algorithm provides a trade-off between monitoring velocity and tracking precision. Fractional calculus is used to provide an enhanced dynamical model of the PV system to describe nonlinear characteristics. Moreover, three metaheuristic optimization techniques are applied; Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and AntLion Optimizer (ALO) are used for tuning the FO parameters of the proposed INC-MPPT. A MATLAB-Simulink-based model of the PV and optimization have been developed and simulated for different INC-MPPT techniques. Different techniques aim to control the boost DC-DC converter towards the MPP. The proposed optimization algorithms are, also, developed and implemented in MATLAB to tune the target parameters. Four performance indices are also introduced in this research to show the reliability of the comparative analysis of the proposed FO-INC with metaheuristic optimization and the conventional INC-MPPT algorithms when applied to a dynamical PV system under rapidly changing weather conditions. The simulation results show the effective performance of the proposed metaheuristic optimized FO-INC as a MPPT control for different climatic conditions with disturbance rejection and robustness analysis.

Suggested Citation

  • Hossam Hassan Ammar & Ahmad Taher Azar & Raafat Shalaby & M. I. Mahmoud, 2019. "Metaheuristic Optimization of Fractional Order Incremental Conductance (FO-INC) Maximum Power Point Tracking (MPPT)," Complexity, Hindawi, vol. 2019, pages 1-13, November.
    • Handle: RePEc:hin:complx:7687891
    DOI: 10.1155/2019/7687891
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    References listed on IDEAS

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    1. Mehdi Seyedmahmoudian & Tey Kok Soon & Elmira Jamei & Gokul Sidarth Thirunavukkarasu & Ben Horan & Saad Mekhilef & Alex Stojcevski, 2018. "Maximum Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions Using Bat Algorithm," Sustainability, MDPI, vol. 10(5), pages 1-16, April.
    2. Lan Li & Hao Wang & Xiangping Chen & Abid Ali Shah Bukhari & Wenping Cao & Lun Chai & Bing Li, 2019. "High Efficiency Solar Power Generation with Improved Discontinuous Pulse Width Modulation (DPWM) Overmodulation Algorithms," Energies, MDPI, vol. 12(9), pages 1-18, May.
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    Cited by:

    1. Ahmad Taher Azar & Azher M. Abed & Farah Ayad Abdulmajeed & Ibrahim A. Hameed & Nashwa Ahmad Kamal & Anwar Jaafar Mohamad Jawad & Ali Hashim Abbas & Zainab Abdulateef Rashed & Zahraa Sabah Hashim & Mo, 2022. "A New Nonlinear Controller for the Maximum Power Point Tracking of Photovoltaic Systems in Micro Grid Applications Based on Modified Anti-Disturbance Compensation," Sustainability, MDPI, vol. 14(17), pages 1-25, August.
    2. Srinivasan Vadivel & Boopathi C. Sengodan & Sridhar Ramasamy & Mominul Ahsan & Julfikar Haider & Eduardo M. G. Rodrigues, 2022. "Social Grouping Algorithm Aided Maximum Power Point Tracking Scheme for Partial Shaded Photovoltaic Array," Energies, MDPI, vol. 15(6), pages 1-17, March.
    3. Edwige Raissa Mache Kengne & Alain Soup Tewa Kammogne & Martin Siewe Siewe & Thomas Tatietse Tamo & Ahmad Taher Azar & Ahmed Redha Mahlous & Mohamed Tounsi & Zafar Iqbal Khan, 2023. "Bifurcation Analysis of a Photovoltaic Power Source Interfacing a Current-Mode-Controlled Boost Converter with Limited Current Sensor Bandwidth for Maximum Power Point Tracking," Sustainability, MDPI, vol. 15(7), pages 1-19, March.
    4. Sanaz Jalali Zand & Kuo-Hsien Hsia & Naser Eskandarian & Saleh Mobayen, 2021. "Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions," Energies, MDPI, vol. 14(5), pages 1-14, February.

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