Enhancing Power Quality in PV-SOFC Microgrids Using Improved Particle Swarm Optimization

Authors

  • V. V. Prabhakaran Department of Electrical and Electronics Engineering, SIMATS, India
  • A. Singh Bharat Sanchar Nigam Limited, India
Volume: 9 | Issue: 5 | Pages: 4616-4622 | October 2019 | https://doi.org/10.48084/etasr.2963

Abstract

The concept of hybrid microgrid (MG) has attracted tremendous attention in modern electricity markets, owing to the enhanced efficiency and reliability it offers to the main electricity grid. Numerous meritorious aspects associated with hybrid MGs are the key features of future large scale renewable technologies. In this paper, a hybrid MG using PV-SOFC (PhotoVoltaic – Solid Oxide Fuel Cell) is connected to an infinite bus bar, in order to achieve an autonomous working mode. The dynamic and steady-state operation with control strategies for both PV and SOFC power systems are analyzed. The objective is to control the voltage and frequency of the MG when it is not connected to the main grid. Typically, an efficient control strategy must assess the power conversion system and its state, in the isolated MG. Moreover, it must reliably handle variant and intermittent type of loads. With this viewpoint, we propose a Voltage Source Inverter (VSI) based Proportional Integral (PI) controller, optimized by Improved Particle Swarm Optimization (IPSO) for the purpose of smooth power flow control improving power quality. The performance of PI-IPSO and PI technologies are evaluated, for the proposed MG, in MATLAB/Simulink. The results obtained verify the effectiveness of the modified PSO algorithm, in comparison to the conventional PI techniques, for the frequency and voltage control of the MG.

Keywords:

Solid Oxide Fuel Cell (SOFC), Voltage Source Inverter (VSI), Proportional Integral Controller (PI), Microgrid (MG), Improved Particle Swarm Optimization (IPSO), Distributed Generators (DG)

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How to Cite

[1]
V. V. Prabhakaran and A. Singh, “Enhancing Power Quality in PV-SOFC Microgrids Using Improved Particle Swarm Optimization”, Eng. Technol. Appl. Sci. Res., vol. 9, no. 5, pp. 4616–4622, Oct. 2019.

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