ROAC: Recursive optimization of Ant colony assisted perturb and observe for a photo voltaic resonant boost converter

  • Authors

    • Selvakumar R
    • M Sujatha
    • S Palanikumar
    2017-12-31
    https://doi.org/10.14419/ijet.v7i1.3.10661
  • Photovoltaic system, Ant Colony Optimization, Perturb and Observe optimization, MPPT, Partial Shading Conditions, MATLAB Simulink

  • Abstract

    This paper introduces a new Hybrid MPPT algorithm by combining new Ant Colony Optimization (ACO) and Perturb & Observe (P&O) method. The maximum power from a solar panel is extracted from all conditions like solar irradiance variation, temperature variation and partial shading conditions. Ant Colony Optimization (ACO) method tracks maximum power from panel under all variations and Perturb & Observe algorithm used in final stage to achieve faster MPP tracking. This proposed algorithm is implemented both in Simulink and hardware. A 5kWp grid connected solar photovoltaic power plant is designed and implemented for the 15 stage 31 level Cascaded Multilevel Inverter (CMLI) with the Selective harmonic elimination algorithm. From the analysis of results, it is found that the proposed hybrid MPPT provides higher MPP tracking performance in any weather conditions compared with other MPPT algorithms

  • References

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  • How to Cite

    R, S., Sujatha, M., & Palanikumar, S. (2017). ROAC: Recursive optimization of Ant colony assisted perturb and observe for a photo voltaic resonant boost converter. International Journal of Engineering & Technology, 7(1.3), 150-156. https://doi.org/10.14419/ijet.v7i1.3.10661

    Received date: 2018-03-26

    Accepted date: 2018-03-26

    Published date: 2017-12-31