Skip to main content

Solar Cell Parameter Extraction by Using Harris Hawks Optimization Algorithm

  • Chapter
  • First Online:
Bio-inspired Neurocomputing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 903))

Abstract

Solar energy is growing faster in this modern era. Many researchers have been attracted towards the research on solar energy because it is a clean source of energy. Mostly two problems are occurred to generate energy from this source: (a) having a beneficial model to characterize solar cells and (b) very less available information about PV cells. Due to these issues, PV module performance affected. In order to extract the parameters of the PV cells and modules, numerous algorithms have been suggested. Many of them often fail to find the best solutions. In this chapter, an application of Harris hawks optimization (HHO) algorithm is reported to extract solar cell parameters. The wide applicability of this algorithm has already been examined over different conventional benchmark functions and on some real problem. This fact motivated authors to implement this algorithm on parameter extraction problem. The main motivation behind the implementation of HHO on solar cell parameter extraction is the efficacy of this algorithm to deal with complex optimization problems. Results of HHO are compared with other well-known algorithm results which shows that HHO produces better results.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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

References

  1. Oliva, D., Cuevas, E., Pajares, G.: Parameter identification of solar cells using artificial bee colony optimization. Energy 72, 93–102 (2014)

    Article  Google Scholar 

  2. Chauhan, A., Saini, R.P.: A review on integrated renewable energy system based power generation for stand-alone applications: configurations, storage options, sizing methodologies and control. Renew. Sustain. Energy Rev. 38, 99–120 (2014)

    Google Scholar 

  3. Energy at the crossroads (2015). http://home.cc.umanitoba.ca/vsmil/pdf_pubs/oecd.pdf

  4. Shafiee, S., Topal, E.: When will fossil fuel reserves be diminished? Energy Policy 37(1), 181–189 (2009)

    Article  Google Scholar 

  5. Apergis, N., Payne, J.E.: Renewable energy, output, CO2 emissions, and fossil fuel prices in Central America: Evidence from a nonlinear panel smooth transition vector error correction model. Energy Econ. 42, 226–232 (2014)

    Article  Google Scholar 

  6. Shivalkar, R.S., Jadhav, H.T., Deo, P.: Feasibility study for the net metering implementation in rooftop solar PV installations across reliance energy consumers. In: 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015], pp. 1–6. IEEE, New York (2015)

    Google Scholar 

  7. Wang, Y., Zhou, S., Huo, H.: Cost and CO2 reductions of solar photovoltaic power generation in China: perspectives for 2020. Renew. Sustain. Energy Rev. 39, 370–380 (2014)

    Article  Google Scholar 

  8. Sundareswaran, K., Peddapati Sankar, P., Srinivasa Rao Nayak, 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 (2014)

    Google Scholar 

  9. The Solar Singularity is Nigh (2015). http://www.greentechmedia.com/articles

  10. Dusonchet, L., Telaretti, E.: Economic analysis of different supporting policies for the production of electrical energy by solar photovoltaics in western European Union countries. Energy Policy 38(7), 3297–3308 (2010)

    Article  Google Scholar 

  11. Solangi, K.H., Islam, M.R., Saidur, R., Rahim, N.A., Fayaz, H.: A review on global solar energy policy. Renew. Sustain. Energy Rev. 15(4), 2149–2163 (2011)

    Article  Google Scholar 

  12. Branker, K., Pearce, J.M.: Financial return for government support of largescale thin-film solar photovoltaic manufacturing in Canada. Energy Policy 38(8), 4291–4303 (2010)

    Article  Google Scholar 

  13. Campoccia, A., Dusonchet, L., Telaretti, E., Zizzo, G.: Comparative analysis of different supporting measures for the production of electrical energy by solar PV and Wind systems: four representative European cases. Sol. Energy 83(3), 287–297 (2009)

    Article  Google Scholar 

  14. Timilsina, G.R., Kurdgelashvili, L., Narbel, P.A.: Solar energy: markets, economics and policies. Renew. Sustain. Energy Rev. 16(1), 449–465 (2012)

    Article  Google Scholar 

  15. Mohamed, M.A., Eltamaly, A.M.: Modeling and Simulation of Smart Grid Integrated with Hybrid Renewable Energy Systems. Springer, Berlin (2018)

    Google Scholar 

  16. Deshmukh, M.K., Deshmukh, S.S.: Modeling of hybrid renewable energy systems. Renew. Sustain. Energy Rev. 12(1), 235–249 (2008)

    Article  Google Scholar 

  17. Shannan, N.M.A.A., Yahaya, N.Z., Singh, B.: Singlediode model and two-diode model of PV modules: A comparison. In: 2013 IEEE International Conference on Control System, Computing and Engineering, pp. 210–214. IEEE, New York (2013)

    Google Scholar 

  18. Ishaque, K., Salam, Z., Taheri, H.: Simple, fast and accurate two-diode model for photovoltaic modules. Sol. Energy Mater. Sol. Cells 95(2), 586–594 (2011)

    Article  Google Scholar 

  19. Shekhawat, S., Saxena, A.: Development and applications of an intelligent crow search algorithm based on opposition based learning. ISA Trans. (2019)

    Google Scholar 

  20. Saxena, A.: A comprehensive study of chaos embedded bridging mechanisms and crossover operators for grasshopper optimisation algorithm. Expert Syst. Appl. 132, 166–188 (2019)

    Article  Google Scholar 

  21. Saxena, A., Shekhawat, S., Kumar, R.: Application and development of enhanced chaotic grasshopper optimization algorithms. Modell. Simul. Eng. 14 p (2018)

    Google Scholar 

  22. Saxena, A., Kumar, R., Mirjalili, S.: A harmonic estimator design with evolutionary operators equipped grey wolf optimizer. Expert Syst. Appl. 145, 113125 (2020)

    Article  Google Scholar 

  23. Appelbaum, J., Peled, A.: Parameters extraction of solar cells–A comparative examination of three methods. Sol. Energy Mater. Sol. Cells 122, 164–173 (2014)

    Article  Google Scholar 

  24. Bai, J., Liu, S., Hao, Y., Zhang, Z., Jiang, M., Zhang, Y.: Development of a new compound method to extract the five parameters of PV modules. Energy Convers. Manag. 79, 294–303 (2014)

    Article  Google Scholar 

  25. Alam, D.F., Yousri, D.A., Eteiba, M.B.: Flower pollination algorithm based solar PV parameter estimation. Energy Convers. Manag. 101, 410–422 (2015)

    Article  Google Scholar 

  26. Chin, V.J., Salam, Z., Ishaque, K.: Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review. Appl. Energy 154, 500–519 (2015)

    Google Scholar 

  27. Jordehi, A.R.: Parameter estimation of solar photovoltaic (PV) cells: A review. Renew. Sustain. Energy Rev. 61, 354–371 (2016)

    Google Scholar 

  28. Jamadi, M., Merrikh-Bayat, F., Bigdeli, M.: Very accurate parameter estimation of single-and double-diode solar cell models using a modified artificial bee colony algorithm. Int. J. Energy Environ. Eng. 7(1), 13–25 (2016)

    Article  Google Scholar 

  29. Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: algorithm and applications. Future Gener. Comput. Syst. 97, 849–872 (2019)

    Google Scholar 

  30. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)

    Article  Google Scholar 

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

    Google Scholar 

  32. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)

    Article  Google Scholar 

  33. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  34. Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Software 114, 163–191 (2017)

    Google Scholar 

  35. Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems crow search algorithm. Comput. Struct. 169, 1–12 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Akash Saxena .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sharma, A., Saxena, A., Shekhawat, S., Kumar, R., Mathur, A. (2021). Solar Cell Parameter Extraction by Using Harris Hawks Optimization Algorithm. In: Bhoi, A., Mallick, P., Liu, CM., Balas, V. (eds) Bio-inspired Neurocomputing. Studies in Computational Intelligence, vol 903. Springer, Singapore. https://doi.org/10.1007/978-981-15-5495-7_20

Download citation

Publish with us

Policies and ethics