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Comparative Study of MPPT Control of Grid-Tied PV Generation by Intelligent Techniques

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Computational Intelligence in Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 711))

Abstract

A grid-tied photovoltaic (PV) system with boost converter is considered for study here. The maximum power point tracking (MPPT) control on the duty cycle of the boost converter is achieved by intelligent techniques such as grey wolf optimization (GWO), Moth-Flame optimization (MFO) and compared with perturb and observe (P&O) method. The proposed approach of MFO reduces the ripples in power, voltage and current and imparts better efficiency under different configurations as compared to latest literature for a similar approach.

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References

  1. Salam, Z., Ahmed, J., Merugu, B.S.: The application of soft computing methods for MPPT of PV system: A technological and status review. Applied Energy, Vol. 107 (2013) 135–148.

    Article  Google Scholar 

  2. Elgendy, M. A., Zahawi, B., Atkinson, D. J.: Assessment of perturb and observe MPPT algorithm implementation techniques for PV pumping applications. IEEE Trans. Sustain. Energy, Vol. 3(1) (Jan. 2012) 21–31.

    Google Scholar 

  3. Elgendy, M. A., Zahawi, B., Atkinson, D. J.: Operating characteristics of the P&O algorithm at high perturbation frequencies for standalone PV systems. IEEE Trans. Energy Convers., Vol. 30(1) (Jun. 2015) 189–198.

    Google Scholar 

  4. Hsieh, G.C., Hsieh, H.I., Tsai, C.Y., Wang, C.H.: Photovoltaic power-increment-aided incremental-conductance MPPT with two-phased tracking. IEEE Transactions on Power Electronics, 28(6) (2013) 2895–2911.

    Article  Google Scholar 

  5. Gheibi, A., Mohammadi, S.M.A, Farsangi, M.M.: A proposed maximum power point tracking by using adaptive fuzzy logic controller for photovoltaic systems. Scientia Iranica. Transaction D, Computer Science & Engineering, Electrical, 23(3) (2016) 1272–1281.

    Google Scholar 

  6. Dallago, E., Liberale, A., Miotti, D., Venchi, G.: Direct MPPT algorithm for PV sources with only voltage measurements. IEEE Transactions on Power Electronics, Vol. 30(12) (2015) 6742–6750.

    Article  Google Scholar 

  7. Hohm, D.P., Ropp, M.E.: Comparative study of maximum power point tracking algorithms. Progress in photovoltaics: Research and Applications, Vol. 11(1) (2003) pp. 47–62.

    Article  Google Scholar 

  8. Ishaque, K., Salam, Z., Amjad, M., Mekhilef, S.: An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation. IEEE transactions on Power Electronics, Vol. 27(8) (2012) 3627–3638.

    Article  Google Scholar 

  9. Sundareswaran, K., Peddapati, S., Palani, S.: MPPT of PV systems under partial shaded conditions through a colony of flashing fireflies. IEEE transactions on energy conversion, 29(2) (2014) 463–472.

    Google Scholar 

  10. Ahmed, J., Salam, Z.: A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability. Applied Energy, Vol. 119(2014) 118–130.

    Article  Google Scholar 

  11. Mirjalili, S., Mirjalili, S. M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Software, Vol. 69 (2014) 46–61.

    Article  Google Scholar 

  12. Mohanty, S., Subudhi, B., Ray, P.K.: A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Transactions on Sustainable Energy, Vol. 7(1) (2016) 181–188.

    Article  Google Scholar 

  13. Mirjalili, S.: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, Vol. 89 (2015) 228–249.

    Article  Google Scholar 

  14. Villalva, M.G., Gazoli, J.R., Ruppert Filho, E.: Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics, Vol. 24(5) (2009) 1198–1208.

    Article  Google Scholar 

  15. Jantsch, M., Real, M., Häberlin, H., Whitaker, C., Kurokawa, K., Blässer. G., Kremer, P., Verhoeve, C.W.: Measurement of PV maximum power point tracking performance. Netherlands Energy Research Foundation ECN, 30 Jun 1997.

    Google Scholar 

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Authors and Affiliations

  1. Veer Surendra Sai University of Technology, Burla, 768018, India

    S. Behera, D. Meher & S. Poddar

Authors
  1. S. Behera
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  2. D. Meher
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  3. S. Poddar
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Corresponding author

Correspondence to S. Behera .

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Editors and Affiliations

  1. Department of Computer Science and Engineering & Information Technology, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering (SSCE), Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Machine Intelligence Research (MIR) Lab, Auburn, WA, USA

    Ajith Abraham

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Behera, S., Meher, D., Poddar, S. (2019). Comparative Study of MPPT Control of Grid-Tied PV Generation by Intelligent Techniques. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_20

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