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Implementation of photovoltaic array MPPT through fixed step predictive control technique

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  • Kakosimos, Panagiotis E.
  • Kladas, Antonios G.

Abstract

This paper proposes the implementation of Photovoltaic (PV) array Maximum Power Point Tracker (MPPT) through Fixed Step-Model Predictive Controller (FS MPC). The proposed controller scheme is based on the modified INcremental Conductance (INC) algorithm combined with the two-step horizon FS MPC. The current based INC algorithm is subject to major modifications in order to be capable of real time interaction between the MPPT and the controller obtaining sufficient information in one sampling time. The developed technique has been incorporated into a model for the overall simulation of the performance of a PV array for solar energy exploitation and is compared to the conventional approach under solar radiation variation improving PV system utilization efficiency and enabling to optimize system performance. This study also illustrates the effectiveness of the proposed controller scheme under various sky conditions with a simulation model employing real solar radiation data.

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  • Kakosimos, Panagiotis E. & Kladas, Antonios G., 2011. "Implementation of photovoltaic array MPPT through fixed step predictive control technique," Renewable Energy, Elsevier, vol. 36(9), pages 2508-2514.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:9:p:2508-2514
    DOI: 10.1016/j.renene.2011.02.021
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    References listed on IDEAS

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    7. Larbes, C. & Aït Cheikh, S.M. & Obeidi, T. & Zerguerras, A., 2009. "Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system," Renewable Energy, Elsevier, vol. 34(10), pages 2093-2100.
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    3. Bouilouta, A. & Mellit, A. & Kalogirou, S.A., 2013. "New MPPT method for stand-alone photovoltaic systems operating under partially shaded conditions," Energy, Elsevier, vol. 55(C), pages 1172-1185.
    4. Patsalides, Minas & Efthymiou, Venizelos & Stavrou, Andreas & Georghiou, George E., 2016. "A generic transient PV system model for power quality studies," Renewable Energy, Elsevier, vol. 89(C), pages 526-542.
    5. Bendib, Boualem & Belmili, Hocine & Krim, Fateh, 2015. "A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 637-648.
    6. Li, Shaowu & Attou, Amine & Yang, Yongchao & Geng, Dongshan, 2015. "A maximum power point tracking control strategy with variable weather parameters for photovoltaic systems with DC bus," Renewable Energy, Elsevier, vol. 74(C), pages 478-488.
    7. Miao Zhang & Keyu Zhuang & Tong Zhao & Jingze Xue & Yunlong Gao & Shuai Cui & Zheng Qiao, 2022. "MPPT Control Algorithm Based on Particle Swarm Optimization and Adaptive Linear Active Disturbance Rejection Control," Energies, MDPI, vol. 15(23), pages 1-19, November.
    8. Qi, Jun & Zhang, Youbing & Chen, Yi, 2014. "Modeling and maximum power point tracking (MPPT) method for PV array under partial shade conditions," Renewable Energy, Elsevier, vol. 66(C), pages 337-345.
    9. Parlak, Koray Sener, 2014. "FPGA based new MPPT (maximum power point tracking) method for PV (photovoltaic) array system operating partially shaded conditions," Energy, Elsevier, vol. 68(C), pages 399-410.
    10. Rajesh, R. & Carolin Mabel, M., 2015. "A comprehensive review of photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 231-248.
    11. Kermadi, Mostefa & Berkouk, El Madjid, 2017. "Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 369-386.

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