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A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems

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  • Ashwin Kumar Devarakonda

    (Department of EEE, Vardhaman College of Engineering, Hyderabad 501218, Telangana, India)

  • Natarajan Karuppiah

    (Department of EEE, Vardhaman College of Engineering, Hyderabad 501218, Telangana, India)

  • Tamilselvi Selvaraj

    (Department of EEE, Sri Sivasubramaniya Nadar College of Engineering, Chennai 603110, Tamil Nadu, India)

  • Praveen Kumar Balachandran

    (Department of EEE, Vardhaman College of Engineering, Hyderabad 501218, Telangana, India)

  • Ravivarman Shanmugasundaram

    (Department of EEE, Vardhaman College of Engineering, Hyderabad 501218, Telangana, India)

  • Tomonobu Senjyu

    (Faculty of Engineering, University of the Ryukyus, Okinawa 903-0213, Japan)

Abstract

The characteristics of a PV (photovoltaic) module is non-linear and vary with nature. The tracking of maximum power point (MPP) at various atmospheric conditions is essential for the reliable operation of solar-integrated power generation units. This paper compares the most widely used maximum power point tracking (MPPT) techniques such as the perturb and observe method (P&O), incremental conductance method (INC), fuzzy logic controller method (FLC), neural network (NN) model, and adaptive neuro-fuzzy inference system method (ANFIS) with the modern approach of the hybrid method (neural network + P&O) for PV systems. The hybrid method combines the strength of the neural network and P&O in a single framework. The PV system is composed of a PV panel, converter, MPPT unit, and load modelled using MATLAB/Simulink. These methods differ in their characteristics such as convergence speed, ease of implementation, sensors used, cost, and range of efficiencies. Based on all these, performances are evaluated. In this analysis, the drawbacks of the methods are studied, and wastage of the panel’s available output energy is observed. The hybrid technique concedes a spontaneous recovery during dynamic changes in environmental conditions. The simulation results illustrate the improvements obtained by the hybrid method in comparison to other techniques.

Suggested Citation

  • Ashwin Kumar Devarakonda & Natarajan Karuppiah & Tamilselvi Selvaraj & Praveen Kumar Balachandran & Ravivarman Shanmugasundaram & Tomonobu Senjyu, 2022. "A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems," Energies, MDPI, vol. 15(22), pages 1-30, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8776-:d:979899
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    References listed on IDEAS

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    1. Wafa Hayder & Dezso Sera & Emanuele Ogliari & Abderezak Lashab, 2022. "On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions," Energies, MDPI, vol. 15(20), pages 1-15, October.
    2. Hyeon-Seok Lee & Jae-Jung Yun, 2019. "Advanced MPPT Algorithm for Distributed Photovoltaic Systems," Energies, MDPI, vol. 12(18), pages 1-17, September.
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    Cited by:

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    3. Matija Bubalo & Mateo Bašić & Dinko Vukadinović & Ivan Grgić, 2023. "Hybrid Wind-Solar Power System with a Battery-Assisted Quasi-Z-Source Inverter: Optimal Power Generation by Deploying Minimum Sensors," Energies, MDPI, vol. 16(3), pages 1-24, February.
    4. Víctor Ferreira Gruner & Jefferson William Zanotti & Walbermark Marques Santos & Thiago Antonio Pereira & Lenon Schmitz & Denizar Cruz Martins & Roberto Francisco Coelho, 2023. "Modified Current Sensorless Incremental Conductance Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 16(2), pages 1-16, January.
    5. Dilip Kumar & Yogesh Kumar Chauhan & Ajay Shekhar Pandey & Ankit Kumar Srivastava & Varun Kumar & Faisal Alsaif & Rajvikram Madurai Elavarasan & Md Rabiul Islam & Raju Kannadasan & Mohammed H. Alshari, 2023. "A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    6. Azhar Ul-Haq & Shah Fahad & Saba Gul & Rui Bo, 2023. "Intelligent Control Schemes for Maximum Power Extraction from Photovoltaic Arrays under Faults," Energies, MDPI, vol. 16(2), pages 1-24, January.

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