A GA trained ANN model for maximum power point tracking in solar photo voltaic systems

  • Authors

    • V. Dega Rajaji
    • K. Chandra Sekhar
    https://doi.org/10.14419/ijet.v7i1.9.10383

    Received date: March 21, 2018

    Accepted date: March 21, 2018

    Published date: March 1, 2018

  • MPPT, GA-ANN, ANN, ANFIS, Insolation, Temperature, Solar PV Cell.

  • Abstract

    Sunlight based vitality is one of the imperative segment of sustainable power source assets and reaping of sun oriented vitality is an actually difficult assignment. Sunlight based PV cells display non liner conduct and their execution is affected by an assortment of components. Fluctuating insolation and temperature assumes a critical part in characterizing the working purpose of the PV cell. Most extreme Power Point Tracking (MPPT) calculations are important to amplify the yield control. The target of getting MPP in PV frameworks is to manage the real working voltage of PV boards. The fundamental reason for acquiring MPP in PV frameworks is to direct the real working voltage of PV boards. In this paper a Genetic Algorithm (GA) –Artificial Neural Network (ANN) MPPT approach is presented. GA is employed to adaptively change the weights during the course of ANN training. The proposed approach is validated by comparing its performance against a data set which contains voltages forecast by ANN approach, Adaptive Neuro Fuzzy (ANFIS) approach and reference voltage obtained through mathematical modeling. The results demonstrate the suitability of the proposed approach in maximum power point tracking through its better performance when compared to the other two approaches. Pearson product-moment correlation has also been calculated to compare the correlation between the predicted voltage and the reference voltage.

  • References

    1. Economic Survey of India, 2014-15
    2. Jawaharlal Nehru National Solar Mission Document. By Ministry of New and Renewable energy Government of India.
    3. Esram, T., & Chapman, P. (2007). Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on Energy Conversion, 22(2), 439–449. https://doi.org/10.1109/TEC.2006.874230.
    4. Ali, A., Saied, M., Mostafa, M., &Moneim, T. (2012). A survey of maximum PPT techniques of PV Systems. Energytech, 2012 IEEE. https://doi.org/10.1109/EnergyTech.2012.6304652.
    5. Jusoh, A., Sutikno, T., Guan, T. K., &Mekhilef, S. (2014). A Re-view on favourable maximum power point tracking systems in solar energy application. Telkomnika, 12(1), 6–22. https://doi.org/10.12928/telkomnika.v12i1.2.
    6. Kamarzaman, N., & Tan, C. W. (2014). A comprehensive review of maximum power point tracking algorithms for photovoltaic systems. Renewable and Sustainable Energy Reviews, 37, 585–598. https://doi.org/10.1016/j.rser.2014.05.045.
    7. Liu, Y., Chen, J., & Huang, J. (2015). A review of maximum power point tracking techniques for use in partially shaded conditions. Re-newable and Sustainable Energy Reviews, 41, 436–453. https://doi.org/10.1016/j.rser.2014.08.038.
    8. Lyden, S., &Haque, M. E. (2015). ” Maximum Power Point Track-ing techniques for photovoltaic systems: A comprehensive review and comparative analysis,” Vol. 52, (pp.1504–1518). https://doi.org/10.1016/j.rser.2015.07.172.
    9. S. Lee, J.-E. Kim, and H. Cha, “Design and implementation of pho-tovoltaic power conditioning system using a current-based maxi-mum power point tracking,” Journal of Electrical Engineering & Technology, vol. 5, no. 4, pp. 606–613, 2010. https://doi.org/10.5370/JEET.2010.5.4.606.
    10. M. G. Villalva, T. G. De Siqueira, and E. RuppertFilho, “Voltage regulation of photovoltaic arrays: small-signal analysis and control design,” IET Power Electronics, vol. 3, no. 6, pp. 869–880, 2010. https://doi.org/10.1049/iet-pel.2008.0344.
    11. W. Xiao, P. R. Palmer, A. Capel, and W. G. Dunford, “Regulation of photovoltaic voltage,” IEEE Transactions on Industrial Electron-ics, vol. 54, no. 3, pp. 1365–1374, 2007. View at Publisher https://doi.org/10.1109/TIE.2007.893059.
    12. W. Xiao, N. Ozog, and W. G. Dunford, “Topology study of photo-voltaic interface for maximum power point tracking,” IEEE Trans-actions on Industrial Electronics, vol. 54, no. 3, pp. 1696–1704, 2007. https://doi.org/10.1109/TIE.2007.894732.
    13. M. A. S. Masoum, H. Dehbonei, and E. F. Fuchs, “Theoretical and experimental analyses of photovoltaic systems with voltage- and current-based maximum power-point tracking,” IEEE Transactions on Energy Conversion, vol. 17, no. 4, pp. 514–522, 2002. https://doi.org/10.1109/TEC.2002.805205.
    14. Libo, W., Zhengming, Z., &Jianzheng, L. (2007). A single-stage three-phase grid-connected photovoltaic system with modified MPPT method and reactive power compensation. IEEE Transac-tions on Energy Conversion, 22(4), 881–886. https://doi.org/10.1109/TEC.2007.895461.
    15. Ewan D. Dunlop, David Halton, “The Performance of Crystalline Silicon Photovoltaic Solar Modules after 22 Years of Continuous Outdoor Exposure”, Prog. Photovolt: Res. Appl. 2006; 14:53–64. https://doi.org/10.1002/pip.627.
    16. BasimAlsayid and JafarJallad, “Modeling and Simulation of Photo-voltaic Cells/Modules/Arrays”, International Journal of Research and Reviews in Computer Science (IJRRCS) Vol. 2, No. 6, Decem-ber 2011.
    17. M. Abdulkadir, A. S. Samosir and A. H. M. Yatim, “ Modeling and Simulation based approach of photovoltaic system in Simulink model”.ARPN Journal of Engineering and Applied Sciences, VOL. 7, NO. 5, MAY 2012.
    18. T. Kerekes, R. Teodorescu , M. Liserre, R. Mastromauro, A. Dell’Aquila , “ MPPT algorithm for Voltage Controlled PV Invert-ers”, IEEE Transactions on Industrial Electronics , vol.54, no.2, April 2007, pp. 994-1004.
    19. HairulNissahZainudin, SaadMekhilef, “Comparison Study of Max-imum Power Point Tracker Techniques for PV Systems”, Proceed-ings of the 14th International Middle East Power Systems Confer-ence (MEPCON’10), Cairo University, Egypt, December 19-21, 2010.
    20. M. Chegaar, P. Mialhe, “Effect of atmospheric parameters on the silicon solar cells performance”, Journal of Electron Devices, Vol. 6, 2008, pp. 173-176.
    21. Saren Johnston, “Sunproofing Solar Cells Computer simulations help explain why solar cells degrade in sunlight”, Insider, April 2003.

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  • How to Cite

    Rajaji, V. D., & Sekhar, K. C. (2018). A GA trained ANN model for maximum power point tracking in solar photo voltaic systems. International Journal of Engineering and Technology, 7(1.9), 294-301. https://doi.org/10.14419/ijet.v7i1.9.10383