Skip to main content
SpringerLink
Log in

Nature-Inspired Algorithms for Maximum Power Point Tracking in Photovoltaic Systems Under Partially Shaded Conditions

  • Chapter
  • First Online:
Intelligent Paradigms for Smart Grid and Renewable Energy Systems

Part of the book series: Algorithms for Intelligent Systems ((AIS))

  • 430 Accesses

Abstract

Solar photovoltaic (PV) power is a promising alternative to fossil fuel-based power because of its attributes such as omnipresence, abundant availability, absence of rotating parts, modular and low maintenance cost. Due to the low conversion efficiency and higher initial investments on PV systems, maximum power point tracking (MPPT) is an essential requirement in grid-connected PV systems. MPPT in PV systems is a challenging task due to the nonlinear current–voltage (I–V) and power–voltage (P–V) characteristics. MPPT under partially shaded conditions (PSC) is further complicated due to the existence of multiple power peaks in the P–V curve. One of the prominent but cost-effective and feasible method of MPPT is the application of particle swarm optimization (PSO), and this scheme has been extensively reported in the literature. Recently, few more nature-inspired optimization techniques such as genetic algorithm (GA), firefly algorithm (FA) and artificial bee colony (ABC) are also implemented for MPPT. This chapter develops and analyses the performance of few popular nature-inspired optimization techniques towards MPPT. The procedure for implementation of each method is lucidly explained and computed MPPT curves along with qualitative and numeric data are presented. Experimental implementation is also included for completeness.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Koutroulis, E., Kalaitzakis, K., Voulgaris, N.C.: Development of a microcontroller-based photovoltaic maximum power point tracking control system. IEEE Trans. Power Electron. 16(1), 46–54 (2001)

    Article  Google Scholar 

  2. Masoum, M.A., Dehbonei, H., Fuchs, E.F.: Theoretical and experimental analyses of photovoltaic systems with voltage and current-based maximum power point tracking. IEEE Power Eng. Rev. 22(8), 62–62 (2002)

    Article  Google Scholar 

  3. Noguchi, T., Togashi, S., Nakamoto, R.: Short-current pulse-based maximum-power-point tracking method for multiple photovoltaic-and converter module system. IEEE Trans. Ind. Electron. 49(1), 217–223 (2002)

    Article  Google Scholar 

  4. Femia, N., Petrone, G., Spagnuolo, G., Vitelli, M.: Optimization of perturb and observe maximum power point tracking method. IEEE Trans. Power Electron. 20(4), 963–973 (2005)

    Article  Google Scholar 

  5. Mei, Q., Shan, M., Liu, L., Guerrero, J.M.: A novel improved variable step-size incremental-resistance MPPT method for PV systems. IEEE Trans. Ind. Electron. 58(6), 2427–2434 (2010)

    Article  Google Scholar 

  6. Brunton, S.L., Rowley, C.W., Kulkarni, S.R., Clarkson, C.: Maximum power point tracking for photovoltaic optimization using ripple-based extremum seeking control. IEEE Trans. Power Electron. 25(10), 2531–2540 (2010)

    Article  Google Scholar 

  7. Safari, A., Mekhilef, S.: Simulation and hardware implementation of incremental conductance MPPT with direct control method using cuk converter. IEEE Trans. Ind. Electron. 58(4), 1154–1161 (2011)

    Article  Google Scholar 

  8. Eshram, T., Chapman, P.L.: Comparison of photovoltaic array maximum power point tracking techniques. IEEE Trans. Energy Convers. 22(2), 439–450 (2007)

    Article  Google Scholar 

  9. Subudhi, B., Pradhan, R.: A comparative study on maximum power point tracking techniques for photovoltaic power system. IEEE Trans. Sustain. Energy 4(1), 89–98 (2013)

    Article  Google Scholar 

  10. de Brito, M.A.G., Galotto, L., Sampio, L.P., de Azevedo e Melo, G., Canesin, V.A.: Evaluation of the main MPPT techniques for photovoltaic applications. IEEE Trans. Ind. Electron. 60(3), 1156–1167 (2013)

    Article  Google Scholar 

  11. Badram, A., Davoudi, A., Balog, R.S.: Control and circuit techniques to mitigate partial shading effects in photovoltaic arrays. IEEE J. Photovolt. 2(4), 532–546 (2012)

    Article  Google Scholar 

  12. Patel, H., Agarwal, V.: Maximum power point tracking scheme for PV systems operating under partially shaded conditions. IEEE Trans. Ind. Electron. 55(4), 1689–1698 (2008)

    Article  Google Scholar 

  13. Nguyen, T.L., Low, K.S.: A global maximum power point tracking scheme employing DIRECT search algorithm for photovoltaic systems. IEEE Trans. Ind. Electron. 57(10), 3456–3467 (2010)

    Article  Google Scholar 

  14. Kobayashi, K., Takano, I., Sawada, Y.: A study of a two stage maximum power point tracking control of a photovoltaic system under partially shaded insolation conditions. Sol. Energy Mater. Sol. Cells 90(18/19), 2975–2988 (2006)

    Article  Google Scholar 

  15. Alajmi, B.N., Ahmed, K.H., Finney, S.J., Williams, B.W.: Fuzzy- logic-control approach of a modified hill-climbing method for maximum power point in microgrid standalone photovoltaic system. IEEE Trans. Power Electron. 26(4), 1022–1030 (2011)

    Article  Google Scholar 

  16. Rai, A.K., Kaushika, N.D., Singh, B., Agarwal, N.: Simulation model of ANN based maximum power point tracking controller for solar PV system. Sol. Energy Mater. Sol. Cells 95, 773–778 (2011)

    Article  Google Scholar 

  17. Miyatake, M., Veerachary, M., Toriumi, F., Fujii, N., Ko, H.: Maximum power point tracking of multiple photovoltaic arrays: a PSO approach. IEEE Trans. Aerosp. Electron. Syst. 47(1), 367–380 (2011)

    Article  Google Scholar 

  18. Liu, Y.H., Huang, S.C., Huang, J.W., Liang, W.C.: A particle swarm optimization-based maximum power point tracking algorithm for PV systems operating under partially shaded conditions. IEEE Trans. Energy Conver. 27(4), 1027–1035 (2012)

    Article  Google Scholar 

  19. Ishaque, K., Salam, Z.: A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition. IEEE Trans. Ind. Electron 60(8), 3195–3206 (2013)

    Google Scholar 

  20. Daraban, S., Petreus, D., Morel, C.: A novel global MPPT based on genetic algorithms for photovoltaic systems under the influence of partial shading. In: 39th Annual IEEE IECON 2013, pp. 1490–1495. Vienna, 10–13 Nov 2013

    Google Scholar 

  21. Sundareswaran, K., Sankar, P., Palani, S.: MPPT of PV systems under partial shaded conditions through a colony of flashing fireflies. IEEE Trans. Energy. Convers. 29(2), 463–472 (2014)

    Article  Google Scholar 

  22. Sundareswaran, K., Sankar, P., Nayak, P.S.R., Simon, S.P., Palani, S.: Enhanced energy output from a PV system under partial shaded conditions through artificial bee colony. IEEE Trans. Sustain. Energy 6(1), 198–209 (2015)

    Article  Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. Patel, H., Agarwal, V.: MATLAB-based modeling to study the effects of partial shading on PV array characteristics. IEEE Trans. Energy Convers. 23(1), 302–310 (2008)

    Article  Google Scholar 

  25. Alajmi, B.N., Ahmed, K.H., Finney, S.J., Williams, B.W.: A maximum power point tracking technique for partially shaded photovoltaic systems in microgrids. IEEE Trans. Ind. Electron. 60(4), 1596–1606 (2013)

    Article  Google Scholar 

  26. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)

    Google Scholar 

  27. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  28. Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 303–308 (1997)

    Google Scholar 

  29. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report TR06. Engineering Faculty, Computer Engineering Department, Erciyes University, Turkey, Oct 2005

    Google Scholar 

  30. Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization for solving constrained optimization problems. In: Lecture notes in computer science, vol. 4529, pp. 789–798. Springer (2007)

    Google Scholar 

  31. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)

    Article  Google Scholar 

  32. Yang, X.S., Deb, S.: Cuckoo search via levy flights. In: Proceedings of the NaBIC, pp. 210–214 (2009)

    Google Scholar 

  33. Yang, X.S., Deb, S.: Engineering optimization by cuckoo search. Int. J. Math. Model. Numer. Optim. 1, 330–343 (2010)

    MATH  Google Scholar 

  34. Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. 26(2), 29–41 (1996)

    Article  Google Scholar 

  35. Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artif. Life 5(2), 137–172 (1999)

    Google Scholar 

  36. Dorigo, M., Stutzle, T.: Ant Colony Optimization. Prentice-Hall of India Pvt. Limited, New Delhi, India (2005)

    MATH  Google Scholar 

  37. Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Oxford, England (1975)

    Google Scholar 

  38. Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA (1989)

    MATH  Google Scholar 

  39. Beasley, D., Bull, D.R., Martin, R.R.: An overview of genetic algorithms: part I—fundamentals. Univ. Comput. 15(2), 58–69 (1993)

    Google Scholar 

  40. Srinivas, M., Patnaik, L.M.: Genetic algorithms: a survey. Computer 27, 17–26 (1994)

    Article  Google Scholar 

  41. Man, K.F., Tang, K.S., Kwong, S.: Genetic algorithms: concepts and applications. IEEE Trans. Ind. Electron. 43(5), 519–534 (1996)

    Article  Google Scholar 

  42. Yang, X.S.: Nature-inspired metaheuristic algorithm. Luniver Press, Beckington, UK (2008)

    Google Scholar 

  43. Yang, X.S.: Firefly algorithms for multimodal optimization. Stoch. Alg. Found. Appl. (SAGA) 5792, 169–178 (2009)

    MathSciNet  MATH  Google Scholar 

  44. Yang, X.S.: Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-Inspired Comput. 2, 78–84 (2010)

    Article  Google Scholar 

  45. Jain, S.: Agarval V: Comparison of the performance of maximum power point tracking schemes applied to single-stage grid-connected photovoltaic systems. IET Electr. Power Appl. 1(5), 753–762 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka Surathkal, Mangalore, India

    V. Vignesh Kumar

  2. Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India

    C. K. Aravind

Authors
  1. V. Vignesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. K. Aravind
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Vignesh Kumar .

Editor information

Editors and Affiliations

  1. Department of Information Technology, PSG College of Technology, Coimbatore, Tamil Nadu, India

    B. Vinoth Kumar

  2. Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

    P. Sivakumar

  3. Department of Electrical and Electronics Engineering, National Institute of Technology Puducherry, Karaikal, Puducherry, India

    M.M. Rajan Singaravel

  4. Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, Tamil Nadu, India

    K. Vijayakumar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Vignesh Kumar, V., Aravind, C.K. (2021). Nature-Inspired Algorithms for Maximum Power Point Tracking in Photovoltaic Systems Under Partially Shaded Conditions. In: Vinoth Kumar, B., Sivakumar, P., Rajan Singaravel, M., Vijayakumar, K. (eds) Intelligent Paradigms for Smart Grid and Renewable Energy Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-9968-2_9

Download citation

Publish with us

Policies and ethics

Search

Navigation