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System Parameter Based Performance Optimization of Solar PV Systems with Perturbation Based MPPT Algorithms

Author

Listed:
  • Sachin Angadi

    (Department of Electrical and Electronics Engineering, K.L.E Technological University, Hubli 580031, India)

  • Udaykumar R. Yaragatti

    (Department of Electrical and Electronics Engineering, National Institute of Technology—Karnataka, Surathkal 575025, India)

  • Yellasiri Suresh

    (Department of Electrical and Electronics Engineering, National Institute of Technology—Karnataka, Surathkal 575025, India)

  • A. B. Raju

    (Department of Electrical and Electronics Engineering, K.L.E Technological University, Hubli 580031, India)

Abstract

Maximum power point tracking (MPPT) algorithms are invariably employed to utilize solar photovoltaic (PV) systems effectively. Perturbation based MPPT algorithms are popular due to their simplicity and reasonable efficiency. While novel MPPT algorithms claim increased energy utilization over classic perturbation techniques, their performance is governed by the choice of optimal algorithm parameters. Existing guidelines for parameter optimization are mathematically laborious and are not generic. Hence, this paper aims to provide simple and comprehensive guidelines to ensure optimal performance from the perturbation based MPPT technique. For an illustration of proposed claims, a solar PV fed boost converter is investigated to examine the effect of input capacitor, digital filter cut-off frequency, system time constant and sampling time on implementing a classic Perturb and Observe (P and O) algorithm. The readers will be presented with two simple step tests (to determine the effective system time constant) and proposed guidelines to choose the optimal performance sampling time. Necessary laboratory experiments show that an appropriate choice of sampling time could increase efficiency and ensure system stability. This investigation’s learnings can be easily extended to any power electronics converter, loads and all perturbation-based algorithms used in solar PV systems. The results of appropriate tests on the system’s mathematical model and the laboratory prototype are presented in detail to support this research’s claims.

Suggested Citation

  • Sachin Angadi & Udaykumar R. Yaragatti & Yellasiri Suresh & A. B. Raju, 2021. "System Parameter Based Performance Optimization of Solar PV Systems with Perturbation Based MPPT Algorithms," Energies, MDPI, vol. 14(7), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:2007-:d:530467
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    References listed on IDEAS

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    1. Teuvo Suntio & Alon Kuperman, 2019. "Maximum Perturbation Step Size in MPP-Tracking Control for Ensuring Predicted PV Power Settling Behavior," Energies, MDPI, vol. 12(20), pages 1-19, October.
    2. Mostafa Ahmed & Mohamed Abdelrahem & Ibrahim Harbi & Ralph Kennel, 2020. "An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems," Energies, MDPI, vol. 13(24), pages 1-25, December.
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    Cited by:

    1. Timmidi Nagadurga & Pasumarthi Venkata Ramana Lakshmi Narasimham & V. S. Vakula & Ramesh Devarapalli & Fausto Pedro García Márquez, 2021. "Enhancing Global Maximum Power Point of Solar Photovoltaic Strings under Partial Shading Conditions Using Chimp Optimization Algorithm," Energies, MDPI, vol. 14(14), pages 1-23, July.
    2. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.

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