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Improvement of Self-Predictive Incremental Conductance Algorithm with the Ability to Detect Dynamic Conditions

Author

Listed:
  • Sanaz Jalali Zand

    (Faculty of Electrical and Computer Engineering, Semnan University, Semnan 3513119111, Iran)

  • Kuo-Hsien Hsia

    (Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Naser Eskandarian

    (Faculty of Electrical and Computer Engineering, Semnan University, Semnan 3513119111, Iran)

  • Saleh Mobayen

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
    Department of Electrical Engineering, University of Zanjan, Zanjan 4537138791, Iran)

Abstract

This paper presents a new version of the incremental conductance algorithm for more accurate tracking of the maximum power point (MPP). The modified algorithm is called self-predictive incremental conductance (SPInC), and it recognizes the operational region. It is capable of detecting dynamic conditions, and it detects sudden changes in power resulting from changes in the intensity of radiation or temperature. By selecting the appropriate step size, it obtains maximum power from the panel at any moment. The improved algorithm reduces output power ripple and increases the efficiency of the system by detecting the operating area and selecting the appropriate step size for each region. The SPInC algorithm divides the system’s work areas into three operating zones. It calculates the size of the appropriate step changes for each region after identifying the regions, which allows for more accurate tracking of the MPP and increases the system efficiency at a speed equal to the speed of the conventional method. These additional operations did not result in a system slowdown in the tracking maximum power. According to the MATLAB/Simulink simulation results, the SPInC algorithm is more efficient than conventional InC, and the ripple output power is reduced. SPInC is also compared to the improved perturb and observe (P&O) algorithm. In general, SPInC can compete with the popular algorithms that have been recently proposed for MPPT in the other researches.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1234-:d:505031
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Amit Kumar Sharma & Rupendra Kumar Pachauri & Sushabhan Choudhury & Ahmad Faiz Minai & Majed A. Alotaibi & Hasmat Malik & Fausto Pedro García Márquez, 2023. "Role of Metaheuristic Approaches for Implementation of Integrated MPPT-PV Systems: A Comprehensive Study," Mathematics, MDPI, vol. 11(2), pages 1-48, January.
    2. Akinyemi Ayodeji Stephen & Kabeya Musasa & Innocent Ewean Davidson, 2022. "Modelling of Solar PV under Varying Condition with an Improved Incremental Conductance and Integral Regulator," Energies, MDPI, vol. 15(7), pages 1-22, March.

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