Skip to main content

Advertisement

SpringerLink
Log in

Grid Connected Photovoltaic Power Generation Control Method Based on Swarm Intelligence Algorithm

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Unipolar and bipolar modulations are widely used in the active power filter of photovoltaic grid-connected inverter. In this paper, the basic modulation strategy, on–off action, influence of operational mode, harmonic current and efficiency of unipolar modulation are compared with the same of bipolar modulation. On this basis, a hybrid modulation strategy integrating advantages of unipolar and bipolar modulations is obtained after analysis, of which effectiveness and feasibility is verified in experiment.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Al-Saedi, W., Lachowicz, S.W., Habibi, D.: (2011) An optimal current control strategy for a three-phase grid-connected photovoltaic system using particle swarm optimization. In: Power Engineering and Automation Conference. IEEE, pp. 286–290 (2011)

  2. Zhou, T., Sun, W.: MPPT control method of PV system with PSO algorithm based on minimal particle angles. In: Proceedings of the 2012 International Conference on Information Technology and Software Engineering. Springer, Heidelberg, pp. 755–763 (2013)

  3. Li, S.Q., Zhang, B., Xu, T.J., et al.: A new MPPT control method of photovoltaic grid-connected inverter system. In: Chinese Control and Decision Conference, pp. 2753–2757 (2014)

  4. Wang, F., Zhang, C.: Protection and control for grid connected photovoltaic power generation system based on instantaneous power theory. In: Pacific-Asia Conference on Circuits, Communications and Systems, Paccs 2009, Chengdu, China, DBLP, pp. 356–359 (2009)

  5. Zheng, L., Liu, S., Qinghe, X.U.: Constant power flow control at grid-connected point of photovoltaic microgrid based on nonlinear diffusion particle swarm optimization. Power Syst. Technol. 34(10), 152–156 (2010)

    Google Scholar 

  6. Mohammadi, H.R., Akhavan, A.: A new control method for grid-connected PV system based on quasi-Z-source cascaded multilevel inverter using evolutionary algorithm. Int. J. Power Electron. Drive Syst. 6(1), 109–120 (2015)

    Google Scholar 

  7. Welch, R., Venayagamoorthy, G.K.: A fuzzy-PSO based controller for a grid independent photovoltaic system. In: Swarm Intelligence Symposium, 2007. Sis. IEEE Xplore, pp. 227–233 (2007)

  8. Hussain, T.N., Sulaiman, S.I., Musirin, I., et al.: A hybrid artificial neural network for grid-connected photovoltaic system output prediction. In: Computers & Informatics. IEEE, pp. 108–111 (2013)

  9. Renaudineau, H., Donatantonio, F., Fontchastagner, J., et al.: A PSO-based global MPPT technique for distributed PV power generation. IEEE Trans. Ind. Electron. 62(2), 1047–1058 (2015)

    Google Scholar 

  10. Gómez, M., Jurado, F., Díaz, P., et al.: Evaluation of a particle swarm optimization based method for optimal location of photovoltaic grid-connected systems. Electr. Power Compon. Syst. 38(38), 1123–1138 (2010)

    Google Scholar 

  11. Souamy, L.: Research on the MPPT stability of the single-stage and single-phase photovoltaic grid-connected system. Appl. Mech. Mater. 263–266, 2252–2257 (2012)

    Google Scholar 

  12. Ebrahim, M.A., Abdelhadi, H.A., Mahmoud, H.M., et al.: Optimal design of MPPT controllers for grid connected photovoltaic array system. Int. J. Emerg. Electr. Power Syst. 17(5), 511–517 (2016)

    Google Scholar 

  13. Kornelakis, A.: Multiobjective particle swarm optimization for the optimal design of photovoltaic grid-connected systems. In: The, IEEE International Conference on Information Management and Engineering. IEEE, pp. 27–30 (2010)

  14. Bhandari, B., Lee, K.T., Lee, G.Y., et al.: Optimization of hybrid renewable energy power systems: a review. Int. J. Precis. Eng. Manuf Green Technol. 2(1), 99–112 (2015)

    Google Scholar 

  15. Chen, L., Zhao, X.: Power control algorithm based on dynamic particle swarm optimization in cognitive radio networks. J. Comput. Inf. Syst. 11(8), 2863–2872 (2015)

    Google Scholar 

  16. Hamza, R., Muhammad, K., Arunkumar, N., González, G.: Hash based encryption for keyframes of diagnostic hysteroscopy. IEEE Access (2017). https://doi.org/10.1109/ACCESS.2017.2762405

    Google Scholar 

  17. Fernandes, S.L., Gurupur, V.P., Sunder, N.R., Arunkumar, N., Kadry, S.: A novel nonintrusive decision support approach for heart rate measurement. Pattern Recogn. Lett. (2017). https://doi.org/10.1016/j.patrec.2017.07.002

    Google Scholar 

  18. Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non focal EEG using entropies. Pattern Recogn. Lett. 94, 112–117 (2017)

    Google Scholar 

  19. Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imaging Health Inform. 6(3), 724–730 (2016)

    Google Scholar 

  20. Arunkumar, N., Ram Kumar, K., Venkataraman, V.: Automatic detection of epileptic seizures using permutation entropy, Tsallis entropy and Kolmogorov complexity. J. Med. Imaging Health Inform. 6(2), 526–531 (2016)

    Google Scholar 

  21. Liu, S., Cai, C., Zhu, Q., Arunkumar, N.: A study of software pools for seismogeology-related software based on the Docker technique. Int. J. Comput. Appl. (2017). https://doi.org/10.1080/1206212X.2017.1396429

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Southeast University, Nanjing, 211100, China

    Chen Xiaoyu, Zheng Jianyong & Mei Jun

Authors
  1. Chen Xiaoyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zheng Jianyong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mei Jun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chen Xiaoyu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiaoyu, C., Jianyong, Z. & Jun, M. Grid Connected Photovoltaic Power Generation Control Method Based on Swarm Intelligence Algorithm. Cluster Comput 22 (Suppl 2), 4491–4501 (2019). https://doi.org/10.1007/s10586-018-2047-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2047-9

Keywords

Search

Navigation