The power-voltage characteristic curve of photovoltaic systems under partially shaded conditions exhibits multiple peaks and renders conventional maximum power point tracking techniques ineffective. This study proposes a hybrid optimisation algorithm incorporating genetic algorithm (GA) in the initial stages of tracking followed by traditional perturb and observe (P&O) algorithm. Although GA and P&O methods do not guarantee convergence to global maximum power point when employed separately, the fusion of the two methods leads to confirmed global convergences with least time. The excellent performance of the combined method is illustrated through extensive simulation and experimental results.

References

1. 1)
  - 2. Sera, D., Mathe, L., Kerekes, T., Spataru, S.V., Teodorescu, R.: ‘On the perturb-and-observe and incremental conductance MPPT methods for PV systems’, IEEE J. Photovoltaics, 2013, 3, (3), pp. 1070–1078 (doi: 10.1109/JPHOTOV.2013.2261118).
2. 2)
  - 5. Xiao, W., Zeineldin, H.H., Zhang, P.: ‘Statistic and parallel testing procedure for evaluating maximum power point tracking algorithms of photovoltaic power systems’, IEEE J. Photovoltaics, 2013, 3, (3), pp. 1062–1069 (doi: 10.1109/JPHOTOV.2013.2257922).
3. 3)
  - D.S. Weile , E. Michielssen . Genetic algorithm optimization applied to electromagnetics: a review. IEEE Trans. Antennas Propag. , 3 , 343 - 353
4. 4)
  - 24. 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., 2011, 47, (1), pp. 367–380 (doi: 10.1109/TAES.2011.5705681).
5. 5)
  - 16. Ishaque, K., Salam, Z.: ‘A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition’, IEEE Trans. Ind. Electron., 2013, 60, (8), pp. 3195–3206.
6. 6)
  - K.F. Man , K.S. Tan , S. Kwong . Genetic algorithms: concepts and applications. IEEE Trans. Ind. Electron. , 5 , 519 - 533
7. 7)
  - 25. 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 Convers., 2012, 27, (4), pp. 1027–1035 (doi: 10.1109/TEC.2012.2219533).
8. 8)
  - 24. Daraban, S., Petreus, D., Morel, C.: ‘A novel global MPPT based on genetic algorithms for photovoltaic systems under the influence of partial shading’. 39th Annual IEEE IECON 2013, Vienna, 10–13 November 2013, pp. 1490–1495.
9. 9)
  - M. Dorigo , A. Colorni . The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst., Man, Cybern. Part B , 1 , 1 - 13
10. 10)
  - 27. Ovaska, S.J., Kamiya, A., Quanchen, Y.: ‘Fusion of soft computing and hard computing: computational structures and characteristic Feature’, IEEE Trans. Syst., Man, Cybern., – C: Appl. Rev., 2006, 32, (3), pp. 439–448 (doi: 10.1109/TSMCC.2005.855528).
11. 11)
  - L. Piegari , R. Rizzo . Adaptive perturb and observe algorithm for photovoltaic maximum power point tracking. IET Renew. Power Gener. , 317 - 328
12. 12)
  - 44. Hsieh, G.-C., Hsieh, H.-I., Tsai, C.-Y., Wang, C.-H.: ‘Photovoltaic power-increment-aided incremental-conductance Mppt with two-phased tracking’, IEEE Trans. Power Electron., 2013, 28, (6), pp. 2895–2911 (doi: 10.1109/TPEL.2012.2227279).
13. 13)
  - 55. de Brito, M., Junior, L., Sampaio, L., Melo, G., Canesin, C.: ‘Evaluation of the main Mppt techniques for photovoltaic applications’, IEEE Trans. Ind. Electron., 2013, 60, (3), pp. 1156–1167 (doi: 10.1109/TIE.2012.2198036).
14. 14)
  - A.G. Bakistzis , P.N. Biskas , C.E. Zoumas , V. Petridis . Optimal power flow by enhanced genetic algorithm. IEEE Trans. Power Syst. , 2 , 229 - 236
15. 15)
  - 28. Ovaska, S.J., VanLandingham, H.F.: ‘Guest editorial: special issue on fusion of soft computing and hard computing in industrial applications’, IEEE Trans. Syst., Man, Cybern. C: Appl. Rev., 2002, 32, (2), pp. 69–71 (doi: 10.1109/TSMCC.2002.801346).
16. 16)
  - M.G. Villalva , J.R. Gazoli , E.R. Filho . Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electron. , 5 , 1198 - 1208
17. 17)
  - N. Bianchi , S. Bolognani . Design optimization of electric motors by genetic algorithms. IEE Proc. B, Electr. Power Appl. , 5 , 475 - 483
18. 18)
  - 9. Goldberg, D.E.: ‘Genetic algorithms in search, optimization, and machine learning’ (Addision-Wesley Publishing Co., 1989).
19. 19)
  - 10. Xingquan, Z., Shiyong, L.: ‘Solving function optimization problems with the immune principle’, J. Syst. Eng. Electron., 2004, 15, (4), pp. 702–709.
20. 20)
  - 29. Ovaska, S.J., VanLandingham, H.F., Kamiya, A.: ‘Fusion of soft computing and hard computing in industrial applications: an overview’, IEEE Trans. Syst., Man, Cybern. C: Appl. Rev., 2002, 32, (2), pp. 72–79 (doi: 10.1109/TSMCC.2002.801354).
21. 21)
  - H. Patel , V. Agarwal . MATLAB-based modeling to study the effects of partial shading on PV array characteristics. IEEE Trans. Energy Convers. , 1 , 302 - 310
22. 22)
  - 7. Ishaque, K., Salam, Z., Amjad, M., Mekhilef, S.: ‘An improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillation’, IEEE Trans. Power Electron., 2012, 27, (8), pp. 3627–3638 (doi: 10.1109/TPEL.2012.2185713).
23. 23)
  - 30. Shi, C., Cheok, A.D.: ‘Performance comparision of fused soft control/hard observer type controller with hard control/hard observer type controller for switched reluctance motors’, IEEE Trans. Syst., Man, Cybern. C: Appl. Rev., 2002, 32, (2), pp. 99–112 (doi: 10.1109/TSMCC.2002.801724).
24. 24)
  - 8. Agamy, M.S., Chi, S., Elasser, A., et al: ‘High-power-density DC–DC converter for distributed PV architectures’, IEEE J. Photovoltaics, 2013, 3, (2), pp. 791–798 (doi: 10.1109/JPHOTOV.2012.2230217).
25. 25)
  - 11. Grefenstette, J.J.: ‘Optimization of control parameters for genetic algorithms’, IEEE Trans. Syst., Man. Cybern, 1986, 16, (1), pp. 122–128 (doi: 10.1109/TSMC.1986.289288).
26. 26)
  - 31. Sterrit, R., Bustard, D.W.: ‘Fusing hard and soft computing for fault management in telecommunications systems’, IEEE Trans. Syst., Man, Cybern. C: Appl. Rev., 2002, 32, (2), pp. 92–98 (doi: 10.1109/TSMCC.2002.801725).
27. 27)
  - 6. Subudhi, B., Pradhan, R.: ‘A comparative study on maximum power point tracking techniques for photovoltaic power systems’, IEEE Trans. Sustain. Energy, 2013, 4, (1), pp. 89–98 (doi: 10.1109/TSTE.2012.2202294).
28. 28)
  - M. Srinivas , L.M. Patnaik . Genetic algorithms: a survey. Computer , 6 , 17 - 27
29. 29)
  - 16. Hejri, M., Mokhtari, H., Azizian, M.R., Ghandhari, M., Soder, L.: ‘On the parameter extraction of a five-parameter double-diode model of photovoltaic cells and modules’, IEEE J. Photovoltaics, 2014, 4, (3), pp. 915–923 (doi: 10.1109/JPHOTOV.2014.2307161).
30. 30)
  - 18. Holland, J.H.: ‘Adaptation in natural and artificial systems’ (U Michigan Press, Oxford, England, 1975).
31. 31)
  - 20. Da Silva, W.G., Acarnley, P.P., Finch, J.W.: ‘Application of genetic algorithms to online tuning of electric drive speed controllers’, IEEE Trans. Ind. Electron., 2000, 47, (1), pp. 217–219 (doi: 10.1109/41.824145).
32. 32)
  - 33. Hejri, M., Mokhtari, H., Azizian, M.R., Soder, L.: ‘An analytical-numerical approach for parameter determination of a five-parameter single-diode model of photovoltaic cells and modules’, Int. J. Sustain. Energy, 2013, 10, pp. 1–15. http://dx.doi.org/10.1080/14786451.2013.863886 (doi: 10.1080/14786451.2013.863886).
33. 33)
  - B.N. Alajmi , K.H. Ahmed , S.J. Finney , B.W. Williams . Fuzzy-logic-control approach of a modified hill-climbing method for maximum power point in microgrid standalone photovoltaic system. IEEE Trans. Power Electron. , 4 , 1022 - 1030
34. 34)
  - 13. Dorigo, M., Stutzle, T.: ‘Ant colony optimization’ (Prentice- Hall of India Pvt. Limited, New Delhi, India, 2005).
35. 35)
  - F.J. Ares-Pena , J.A. Rodriguez-Gonzalez , E. Villanueva-Lopez , S.R. Ren-Garajan . Genetic algorithms in the design and optimization of antenna array patterns. IEEE Trans. Antennas Propag. , 3 , 506 - 510

Development of a hybrid genetic algorithm/perturb and observe algorithm for maximum power point tracking in photovoltaic systems under non-uniform insolation

References

Related content