access icon free Application of random search method for maximum power point tracking in partially shaded photovoltaic systems

The power–voltage (P–V) curve of a photovoltaic (PV) power generation system under partially shaded conditions (PSCs) is largely non-linear and multimodal, and hence, global optimisation techniques are required for maximum power point tracking. A traditional optimisation algorithm is proposed here, namely random search method (RSM) for tracking the global maximum power point in a solar power system under PSC. The RSM is based on the use of random numbers in finding the global optima and is a gradient independent method. The major advantage of RSM is its very simple computational steps, which requires very less memory. The performance of RSM in tracking the peak power is studied for a variety of shading patterns and the tracking performance is compared with two-stage perturb and observe (P&O) and population-based particle swarm optimisation (PSO) methods. The simulation results strongly suggest that the RSM is far superior to two-stage P&O method and better than PSO method. Experimental results obtained from a 120-watt prototype PV system validate the effectiveness of the proposed scheme.

Inspec keywords: gradient methods; particle swarm optimisation; solar power stations; search problems; maximum power point trackers; number theory; photovoltaic power systems

Other keywords: P-V curve; peak power tracking; two-stage perturb and observe; PSC; gradient independent method; global maximum power point tracking; RSM; population-based particle swarm optimisation methods; two-stage P&O method; PSO methods; MPPT; photovoltaic power generation; solar power system; computational steps; partially shaded conditions; random search method; power-voltage curve; power 120 W; global optimisation techniques; random numbers

Subjects: Combinatorial mathematics; DC-DC power convertors; Solar power stations and photovoltaic power systems; Optimisation techniques

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 23. Eberhart, R., Kennedy, J.: ‘A new optimizer using particle swarm theory’. Proc. Sixth Int. Symp. MHS, 1995, pp. 3943.
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
      • 31. Sundareswaran, K., Srinivasa Rao Nayak, P., Durga Venkatesh, Ch.: ‘Induction motor starting dynamics optimization using random search method’. Proc. Advances in Control and Optimization of Dynamic Systems ACODS-2012, 2012, pp. 14.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
      • 27. Ahmed, J., Salam, Z.: ‘A soft computing MPPT for PV system based on cuckoo search algorithm’. Fourth Int. Conf. Power Engineering, Energy and Electrical Drives (POWERENG), 2013, Istanbul, May 2013, pp. 558562.
    27. 27)
    28. 28)
      • 28. Beveridge, G.S.G., Schechter, R.S.: ‘Optimisation: theory and practice’ (McGraw-Hill, New York, 1970).
    29. 29)
    30. 30)
    31. 31)
    32. 32)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2013.0234
Loading

Related content

content/journals/10.1049/iet-rpg.2013.0234
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading