http://iet.metastore.ingenta.com
1887

Optimisation method to find the best switch set topology for reconfiguration of photovoltaic panels

Optimisation method to find the best switch set topology for reconfiguration of photovoltaic panels

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Renewable Power Generation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study presents an optimisation method to find the best switch set (SWS) topology for reconfiguration of photovoltaic (PV) panels. An approach based on particle swarm optimisation is used to find the optimised topology. In the optimisation, an objective function is defined, in which a minimum number of switches and maximum capability for realising different desired configurations are taken into account. Although the result is a SWS with the capability of reconfiguring PV panels in different series–parallel configurations, the algorithm can be extended for deriving different SWSs aimed to realise other configurations. Some experiments were carried out to validate the proposed approach. The results confirm that the optimised SWS not only can implement different desirable configurations with a minimum number of switches but also able to overcome different abnormal conditions by a suitable switching.

References

    1. 1)
      • 1. Digest of United Kingdom energy statistics 2010. Stationery Office, 2010.
    2. 2)
      • 2. Femia, N., Petrone, G., Spagnuolo, G., et al: ‘Power electronics and control techniques for maximum energy harvesting in photovoltaic systems’ (CRC Press, Taylor & Francis Group, Boca Raton, 2013).
    3. 3)
      • 3. 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., 2002, 22, (8), p. 62.
    4. 4)
      • 4. Martínez-Moreno, F., Muñoz, J., Lorenzo, E.: ‘Experimental model to estimate shading losses on PV arrays’, Sol. Energy Mater. Sol. Cells, 2010, 94, (12), pp. 22982303.
    5. 5)
      • 5. Reiter, R.D.D.O., Michels, L., Pinheiro, J.R., et al: ‘Comparative analysis of series and parallel photovoltaic arrays under partial shading conditions’. 2012 10th IEEE/IAS Int. Conf. on Industry Applications, 2012.
    6. 6)
      • 6. García, M.C.A., Herrmann, W., Böhmer, W., et al: ‘Thermal and electrical effects caused by outdoor hot-spot testing in associations of photovoltaic cells’, Prog. Photovolt., Res. Appl., 2003, 11, (5), pp. 293307.
    7. 7)
      • 7. Ghanbari, T.: ‘Permanent partial shading detection for protection of photovoltaic panels against hot spotting’, IET Renew. Power Gener., 2017, 11, (1), pp. 123131.
    8. 8)
      • 8. Bouilouta, A., Mellit, A., Kalogirou, S.: ‘New MPPT method for stand-alone photovoltaic systems operating under partially shaded conditions’, Energy, 2013, 55, pp. 11721185.
    9. 9)
      • 9. Manna, D.L., Vigni, V.L., Sanseverino, E.R., et al: ‘Reconfigurable electrical interconnection strategies for photovoltaic arrays: a review’, Renew. Sustain. Energy Rev., 2014, 33, pp. 412426.
    10. 10)
      • 10. El-Dein, M.Z.S., Kazerani, M., Salama, M.M.A.: ‘Optimal photovoltaic array reconfiguration to reduce partial shading losses’, IEEE Trans. Sustain. Energy, 2013, 4, (1), pp. 145153.
    11. 11)
      • 11. Orozco-Gutierrez, M.L., Spagnuolo, G., Ramirez-Scarpetta, J.M., et al: ‘Optimized configuration of mismatched photovoltaic arrays’, IEEE J. Photovoltaics, 2016, 6, (5), pp. 12101220.
    12. 12)
      • 12. Sanseverino, E.R., Ngoc, T.N., Cardinale, M., et al: ‘Dynamic programming and Munkres algorithm for optimal photovoltaic arrays reconfiguration’, Sol. Energy, 2015, 122, pp. 347358.
    13. 13)
      • 13. Salameh, Z., Dagher, F.: ‘The effect of electrical array reconfiguration on the performance of a PV-powered volumetric water pump’, IEEE Trans. Energy Convers., 1990, 5, (4), pp. 653658.
    14. 14)
      • 14. Baka, M.-I., Catthoor, F., Soudris, D.: ‘Near-Static shading exploration for smart photovoltaic module topologies based on snake-like configurations’, ACM Trans. Embedded Comput. Syst., 2016, 15, (2), pp. 121.
    15. 15)
      • 15. Baka, M., Catthoor, F., Soudris, D.: ‘Smart PV module topology with a snake-like configuration’. Proc. 31st European Photovoltaic Solar Energy Conf. and Exhibition., 2015.
    16. 16)
      • 16. Ngoc, T.N., Phung, Q.N., Tung, L.N., et al: ‘Increasing efficiency of photovoltaic systems under non-homogeneous solar irradiation using improved dynamic programming methods’, Sol. Energy, 2017, 150, pp. 325334.
    17. 17)
      • 17. Horoufiany, M., Ghandehari, R.: ‘An optimal fixed reconfiguration scheme for PV arrays power enhancement under mutual shading conditions’, IET Renew. Power Gener., 2017, 11, (11), pp. 14561463.
    18. 18)
      • 18. Tria, L.A.R., Escoto, M.T., Odulio, C.M.F.: ‘Photovoltaic array reconfiguration for maximum power transfer’. 2009 IEEE Region 10 Conf. (TENCON 2009), 2009.
    19. 19)
      • 19. Chaaban, M.A., Alahmad, M., Neal, J., et al: ‘Adaptive photovoltaic system’. IECON 2010 – 36th Annual Conf. on IEEE Industrial Electronics, 7–10 November 2010, pp. 31923197.
    20. 20)
      • 20. Patnaik, B., Mohod, J., Duttagupta, S.P.: ‘Dynamic loss comparison between fixed-State and reconfigurable solar photovoltaic array’. 2012 38th IEEE Photovoltaic Specialists Conf., 2012.
    21. 21)
      • 21. Viola, F., Romano, P., Miceli, R., et al: ‘Technical and economical evaluation on the Use of reconfiguration systems in some EU countries for PV plants’, IEEE Trans. Ind. Appl., 2017, 53, (2), pp. 13081315.
    22. 22)
      • 22. Caruso, M., Di Noia, L.P., Romano, P., et al: ‘Reconfiguration systems: a technical and economic study’, J. Electr. Syst., 2017, 13, (1), pp. 5573.
    23. 23)
      • 23. Storey, J., Wilson, P.R., Bagnall, D.: ‘The optimized-string dynamic photovoltaic array’, IEEE Trans. Power Electron., 2014, 29, (4), pp. 17681776.
    24. 24)
      • 24. Sammut, C., Webb, G.I.: ‘Encyclopedia of machine learning’ (Springer, New York, 2011).
    25. 25)
      • 25. Cai, X., Zeng, J., Tan, Y., et al: ‘Individual parameter selection strategy for particle swarm optimization’ (INTECH Open Access Publisher, 2009).
    26. 26)
      • 26. Kennedy, J., Eberhart, R.: ‘A discrete binary version of the particle swarm algorithm’. 1997 IEEE Int. Conf. on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 1997.
    27. 27)
      • 27. Patnaik, B., Sharma, P., Trimurthulu, E., et al: ‘Reconfiguration strategy for optimization of solar photovoltaic array under non-uniform illumination conditions’. 2011 37th IEEE Photovoltaic Specialists Conf., June 2011, pp. 18591864.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2017.0505
Loading

Related content

content/journals/10.1049/iet-rpg.2017.0505
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address