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Polar coordinated fuzzy controller based real-time maximum-power point control of photovoltaic system

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  • Syafaruddin,
  • Karatepe, Engin
  • Hiyama, Takashi

Abstract

It is crucial to improve the photovoltaic (PV) system efficiency and to develop the reliability of PV generation control systems. There are two ways to increase the efficiency of PV power generation system. The first is to develop materials offering high conversion efficiency at low cost. The second is to operate PV systems optimally. However, the PV system can be optimally operated only at a specific output voltage and its output power fluctuates under intermittent weather conditions. Moreover, it is very difficult to test the performance of a maximum-power point tracking (MPPT) controller under the same weather condition during the development process and also the field testing is costly and time consuming. This paper presents a novel real-time simulation technique of PV generation system by using dSPACE real-time interface system. The proposed system includes Artificial Neural Network (ANN) and fuzzy logic controller scheme using polar information. This type of fuzzy logic rules is implemented for the first time to operate the PV module at optimum operating point. ANN is utilized to determine the optimum operating voltage for monocrystalline silicon, thin-film cadmium telluride and triple junction amorphous silicon solar cells. The verification of availability and stability of the proposed system through the real-time simulator shows that the proposed system can respond accurately for different scenarios and different solar cell technologies.

Suggested Citation

  • Syafaruddin, & Karatepe, Engin & Hiyama, Takashi, 2009. "Polar coordinated fuzzy controller based real-time maximum-power point control of photovoltaic system," Renewable Energy, Elsevier, vol. 34(12), pages 2597-2606.
    • Handle: RePEc:eee:renene:v:34:y:2009:i:12:p:2597-2606
    DOI: 10.1016/j.renene.2009.04.022
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    References listed on IDEAS

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    1. Tafticht, T. & Agbossou, K. & Doumbia, M.L. & Chériti, A., 2008. "An improved maximum power point tracking method for photovoltaic systems," Renewable Energy, Elsevier, vol. 33(7), pages 1508-1516.
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    3. Gabler, Hansjörg, 1998. "Autonomous power supply with photovoltaics: Photovoltaics for rural electrification - reality and vision," Renewable Energy, Elsevier, vol. 15(1), pages 512-518.
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    1. Lalili, D. & Mellit, A. & Lourci, N. & Medjahed, B. & Berkouk, E.M., 2011. "Input output feedback linearization control and variable step size MPPT algorithm of a grid-connected photovoltaic inverter," Renewable Energy, Elsevier, vol. 36(12), pages 3282-3291.
    2. Maatallah, Taher & El Alimi, Souheil & Nassrallah, Sassi Ben, 2011. "Performance modeling and investigation of fixed, single and dual-axis tracking photovoltaic panel in Monastir city, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 4053-4066.
    3. Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
    4. 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.
    5. Ramli, Makbul A.M. & Twaha, Ssennoga & Ishaque, Kashif & Al-Turki, Yusuf A., 2017. "A review on maximum power point tracking for photovoltaic systems with and without shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 144-159.
    6. Datta, Manoj & Senjyu, Tomonobu & Yona, Atsushi & Funabashi, Toshihisa, 2011. "A fuzzy based method for leveling output power fluctuations of photovoltaic-diesel hybrid power system," Renewable Energy, Elsevier, vol. 36(6), pages 1693-1703.
    7. Po-Chen Cheng & Bo-Rei Peng & Yi-Hua Liu & Yu-Shan Cheng & Jia-Wei Huang, 2015. "Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique," Energies, MDPI, vol. 8(6), pages 1-23, June.
    8. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    9. Ishaque, Kashif & Salam, Zainal, 2013. "A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 475-488.
    10. Danandeh, M.A. & Mousavi G., S.M., 2018. "Comparative and comprehensive review of maximum power point tracking methods for PV cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2743-2767.
    11. 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.
    12. Rajesh, R. & Carolin Mabel, M., 2015. "A comprehensive review of photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 231-248.
    13. Bizon, Nicu, 2016. "Global Maximum Power Point Tracking (GMPPT) of Photovoltaic array using the Extremum Seeking Control (ESC): A review and a new GMPPT ESC scheme," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 524-539.
    14. Chun-Liang Liu & Jing-Hsiao Chen & Yi-Hua Liu & Zong-Zhen Yang, 2014. "An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 7(4), pages 1-17, April.

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