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MPPT for a PV Grid-Connected System to Improve Efficiency under Partial Shading Conditions

Author

Listed:
  • Abdulaziz Almutairi

    (Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia)

  • Ahmed G. Abo-Khalil

    (Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia
    Department of Electrical Engineering, College of Engineering, Assuit University, Assuit 71515, Egypt)

  • Khairy Sayed

    (Electrical Engineering Department, Faculty of Engineering, Sohag University, Sohag 82524, Egypt)

  • Naif Albagami

    (Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia)

Abstract

The disadvantage of photovoltaic (PV) power generation is that output power decreases due to the presence of clouds or shade. Moreover, it can only be used when the sun is shining. Consequently, there is a need for further active research into the maximum power point tracking (MPPT) technique, which can maximize the power of solar cells. When the solar cell array is partially shaded due to the influence of clouds or buildings, the solar cell characteristic has a number of local maximum power points (LMPPs). Conventional MPPT techniques do not follow the actual maximum power point, namely, the global maximum power point (GMPP), but stay in the LMPP. Therefore, an analysis of the occurrence of multiple LMPPs due to partial shading, as well as a study on the MPPT technique that can trace GMPP, is needed. In order to overcome this obstacle, the grey wolf optimization (GWO) method is proposed in order to track the global maximum power point and to maximize the energy extraction of the PV system. In addition, opposition-based learning is integrated with the GWO to accelerate the MPPT search process and to reduce convergence time. Simultaneously, the DC link voltage is controlled to reduce sudden variations in voltage in the event of transients of solar radiation and/or temperature. Experimental tests are presented to validate the effectiveness of the proposed MPPT method during uniform irradiance and partial shading conditions. The proposed method is compared with the perturbation and observation method.

Suggested Citation

  • Abdulaziz Almutairi & Ahmed G. Abo-Khalil & Khairy Sayed & Naif Albagami, 2020. "MPPT for a PV Grid-Connected System to Improve Efficiency under Partial Shading Conditions," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10310-:d:459690
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    References listed on IDEAS

    as
    1. Ahmed G. Abokhalil & Ahmed Bilal Awan & Abdel-Rahman Al-Qawasmi, 2018. "Comparative Study of Passive and Active Islanding Detection Methods for PV Grid-Connected Systems," Sustainability, MDPI, vol. 10(6), pages 1-15, May.
    2. Ali Mohamed Eltamaly & Mamdooh Al-Saud & Khairy Sayed & Ahmed G. Abo-Khalil, 2020. "Sensorless Active and Reactive Control for DFIG Wind Turbines Using Opposition-Based Learning Technique," Sustainability, MDPI, vol. 12(9), pages 1-14, April.
    3. Ali M. Eltamaly & M. S. Al-Saud & A. G. Abo-Khalil, 2020. "Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy," Sustainability, MDPI, vol. 12(3), pages 1-20, February.
    4. Karami, Nabil & Moubayed, Nazih & Outbib, Rachid, 2017. "General review and classification of different MPPT Techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 1-18.
    5. Eltamaly, Ali M. & Al-Saud, M.S. & Abokhalil, Ahmed G. & Farh, Hassan M.H., 2020. "Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
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    Cited by:

    1. Reza Alayi & Mahdi Mohkam & Seyed Reza Seyednouri & Mohammad Hossein Ahmadi & Mohsen Sharifpur, 2021. "Energy/Economic Analysis and Optimization of On-Grid Photovoltaic System Using CPSO Algorithm," Sustainability, MDPI, vol. 13(22), pages 1-16, November.
    2. Ibrahim Alsaidan & Priyanka Chaudhary & Muhannad Alaraj & Mohammad Rizwan, 2021. "An Intelligent Approach to Active and Reactive Power Control in a Grid-Connected Solar Photovoltaic System," Sustainability, MDPI, vol. 13(8), pages 1-24, April.
    3. Ahmed G. Abo-Khalil & Walied Alharbi & Abdel-Rahman Al-Qawasmi & Mohammad Alobaid & Ibrahim M. Alarifi, 2021. "Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    4. Duberney Murillo-Yarce & José Alarcón-Alarcón & Marco Rivera & Carlos Restrepo & Javier Muñoz & Carlos Baier & Patrick Wheeler, 2020. "A Review of Control Techniques in Photovoltaic Systems," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    5. Alfredo Gil-Velasco & Carlos Aguilar-Castillo, 2021. "A Modification of the Perturb and Observe Method to Improve the Energy Harvesting of PV Systems under Partial Shading Conditions," Energies, MDPI, vol. 14(9), pages 1-12, April.

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