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On the Effects of Parameter Adjustment on the Performance of PSO-Based MPPT of a PV-Energy Generation System

  • André Luiz Marques LeopoldinoEmail author
  • Cleiton Magalhães Freitas
  • Luís Fernando Corrêa Monteiro
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 269)

Abstract

The growing concern on environmental issues caused by fossil fuels and, indeed, on the availability of such energy resources in a long-run basis have settled the ground for the spreading of the so called green energy sources. Among them, photovoltaic energy stands out due to the possibility of turning practically any household into a micro power plant. One important aspect about this source of energy is that practical photovoltaic generators are equipped with maximum power point tracking (MPPT) systems. Currently, researchers are focused on developing MPPT algorithms for partial shaded panels, among which, particle swarm optimization (PSO) MPPT stands out. PSO is an artificial intelligence method based on the behavior of flock of birds and it works arranging a group of mathematical entities named particles to deal with an optimization problem. Thus, this work focus on analyzing the performance of this algorithm under different design conditions, which means different amount of particles and different set points for the constants. Besides that, the article presents a brief guideline on how to implement PSO-MPPT. Simulations of an array with three photovoltaic panels, boost-converter driven, were carried out in order to back the analyzes.

Keywords

Photovoltaic energy generation Maximum power point tracking Particle swarm optimization 

References

  1. 1.
    Arantegui, R.L., Jäger-Waldau, A.: Photovoltaics and wind status in the European Union after the Paris agreement. Renew. Sustain. Energy Rev. 81, 2460–2471 (2018).  https://doi.org/10.1016/j.rser.2017.06.052CrossRefGoogle Scholar
  2. 2.
    Bendib, B., Belmili, H., Krim, F.: A survey of the most used MPPT methods: conventional and advanced algorithms applied for photovoltaic systems. Renew. Sustain. Energy Rev. 45, 637–648 (2015).  https://doi.org/10.1016/j.rser.2015.02.009CrossRefGoogle Scholar
  3. 3.
    Dolara, A., Leva, S., Manzolini, G.: Comparison of different physical models for PV power output prediction. Sol. Energy 119, 83–99 (2015).  https://doi.org/10.1016/j.solener.2015.06.017CrossRefGoogle Scholar
  4. 4.
    Hasan, M., Parida, S.: An overview of solar photovoltaic panel modeling based on analytical and experimental viewpoint. Renew. Sustain. Energy Rev. 60, 75–83 (2016).  https://doi.org/10.1016/j.rser.2016.01.087CrossRefGoogle Scholar
  5. 5.
    Ishaque, K., Salam, Z., Amjad, M., Mekhilef, S.: An improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillation. IEEE Trans. Power Electron. 27(8), 3627–3638 (2012).  https://doi.org/10.1109/TPEL.2012.2185713CrossRefGoogle Scholar
  6. 6.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks, vol. 4, pp. 1942–1948, November 1995.  https://doi.org/10.1109/ICNN.1995.488968
  7. 7.
    Khare, A., Rangnekar, S.: A review of particle swarm optimization and its applications in solar photovoltaic system. Appl. Soft Comput. 13(5), 2997–3006 (2013).  https://doi.org/10.1016/j.asoc.2012.11.033CrossRefGoogle Scholar
  8. 8.
    Koad, R.B., Zobaa, A.F., El-Shahat, A.: A novel MPPT algorithm based on particle swarm optimization for photovoltaic systems. IEEE Trans. Sustain. Energy 8(2), 468–476 (2017).  https://doi.org/10.1109/TSTE.2016.2606421CrossRefGoogle Scholar
  9. 9.
    Liu, F., Kang, Y., Zhang, Y., Duan, S.: Comparison of P&O and hill climbing MPPT methods for grid-connected PV converter. In: 2008 3rd IEEE Conference on Industrial Electronics and Applications, pp. 804–807, June 2008.  https://doi.org/10.1109/ICIEA.2008.4582626
  10. 10.
    Liu, L., Meng, X., Liu, C.: A review of maximum power point tracking methods of PV power system at uniform and partial shading. Renew. Sustain. Energy Rev. 53, 1500–1507 (2016).  https://doi.org/10.1016/j.rser.2015.09.065CrossRefGoogle Scholar
  11. 11.
    Malinowski, M., Leon, J.I., Abu-Rub, H.: Solar photovoltaic and thermal energy systems: current technology and future trends. Proc. IEEE 105(11), 2132–2146 (2017).  https://doi.org/10.1109/JPROC.2017.2690343CrossRefGoogle Scholar
  12. 12.
    Mirhassani, S.M., Golroodbari, S.Z.M., Golroodbari, S.M.M., Mekhilef, S.: An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time. Int. J. Electr. Power Energy Syst. 64, 761–770 (2015).  https://doi.org/10.1016/j.ijepes.2014.07.074CrossRefGoogle Scholar
  13. 13.
    de Oliveira, F.M., da Silva, S.A.O., Durand, F.R., Sampaio, L.P., Bacon, V.D., Campanhol, L.B.: Grid-tied photovoltaic system based on PSO MPPT technique with active power line conditioning. IET Power Electron. 9(6), 1180–1191 (2016).  https://doi.org/10.1049/iet-pel.2015.0655CrossRefGoogle Scholar
  14. 14.
    Renaudineau, H., et al.: A PSO-based global MPPT technique for distributed PV power generation. IEEE Trans. Ind. Electron. 62(2), 1047–1058 (2015).  https://doi.org/10.1109/TIE.2014.2336600CrossRefGoogle Scholar
  15. 15.
    Rodriguez, E.A., Freitas, C.M., Bellar, M.D., Monteiro, L.F.C.: MPPT algorithm for PV array connected to a hybrid generation system. In: 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE), pp. 1115–1120, June 2015.  https://doi.org/10.1109/ISIE.2015.7281628
  16. 16.
    Sen, T., Pragallapati, N., Agarwal, V., Kumar, R.: Global maximum power point tracking of PV arrays under partial shading conditions using a modified particle velocity-based PSO technique. IET Renew. Power Gener. 12, 555–564 (2018).  https://doi.org/10.1049/iet-rpg.2016.0838CrossRefGoogle Scholar
  17. 17.
    Shepard, N.: Diodes in photovoltaic modules and arrays. Final report, prepared for JPL by General Electric Company Advanced Energy Systems and Technology Division, King of Prussia, Pennsylvania, 15 March 1984Google Scholar
  18. 18.
    Song, M.P., Gu, G.C.: Research on particle swarm optimization: a review. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), vol. 4, pp. 2236–2241, August 2004.  https://doi.org/10.1109/ICMLC.2004.1382171
  19. 19.
    Villalva, M.G., Gazoli, J.R., Filho, E.R.: Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electron. 24(5), 1198–1208 (2009).  https://doi.org/10.1109/TPEL.2009.2013862CrossRefGoogle Scholar
  20. 20.
    Yu, H.J.J., Popiolek, N., Geoffron, P.: Solar photovoltaic energy policy and globalization: a multiperspective approach with case studies of Germany, Japan and China. Prog. Photovolt. Res. Appl. 24(4), 458–476 (2014).  https://doi.org/10.1002/pip.2560. https://onlinelibrary.wiley.com/doi/abs/10.1002/pip.2560CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Rio de Janeiro State UniversityRio de JaneiroBrazil

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