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Hybrid BBO-PSO-based extreme learning machine neural network model for mitigation of harmonic distortions in micro grids

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  • S. Gunasekaran
  • R. Maheswar

Abstract

Microgrid tends to be the cluster of some of the renewable energy sources. The most important research area in the power distribution system side is the improvement in the quality of power delivered to the end users. This paper focuses on enhancing the power quality of the microgrid system; shunt active power filters (SAPF) is employed at the distribution side and to design an appropriate controller that achieves a better compensation for the considered SAPF. It is to be noted that the compensation methodology is dependent on the regulation process of the DC-link voltage. Traditionally, this regulation process is carried out employing a closed loop proportional-integral controller. In this paper, a hybrid biogeography-based optimisation – particle swarm optimization-based extreme learning machine neural network model is proposed to design the compensation for the SAPF and to mitigate the harmonics so that effective power gets delivered through the grid.

Suggested Citation

  • S. Gunasekaran & R. Maheswar, 2020. "Hybrid BBO-PSO-based extreme learning machine neural network model for mitigation of harmonic distortions in micro grids," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 38(4), pages 507-524.
    • Handle: RePEc:ids:ijores:v:38:y:2020:i:4:p:507-524
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