Bacterial Foraging and Seagull Optimization Algorithm based THD level Comparison for Flyback Converter in Grid-connected PV System

Bacterial Foraging and Seagull Optimization Algorithm based THD level Comparison for Flyback Converter in Grid-connected PV System
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© 2022 by IJETT Journal
Volume-70 Issue-6
Year of Publication : 2022
Authors : C Sunil Kumar, Puttamadappa C, Y L Chandrashekar
DOI : 10.14445/22315381/IJETT-V70I6P238

How to Cite?

C Sunil Kumar, Puttamadappa C, Y L Chandrashekar, "Bacterial Foraging and Seagull Optimization Algorithm based THD level Comparison for Flyback Converter in Grid-connected PV System," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 379-394, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P238

Abstract
With intensifying integration of grid and photovoltaic (PV) systems, the power quality (PQ) is a prime constraint in modern power systems. Here, the reactive and active power controller is bid with a three-phase grid integrated PV system to magnify the PQ based on the Seagull Optimization Algorithm (SOA). The proposed system comprises two key controllers: the Flyback converter with Bacterial Foraging Optimization Algorithm (BFOA) to track the utmost powers from the PV panels and the suggested SOA optimized controller for the grid-integrated three-phase inverter. The integration of grid and PV utilizes a three-phase modular multilevel inverter (MMI) that manages the active and reactive powers by optimizing the SOA controller using the grid side voltage. The proposed control strategy minimizes power dropping into the inverter by regulating the instantaneous active and reactive powers to improve PQ. Moreover, it can reduce the harmonic and compensate for reactive power. The simulation of the proposed system is by utilizing the MATLAB/Simulink platform, and its outcomes are analyzed and equated with previously generated methods. The proposed methodology is compared with the conventional methods like Atom Search Algorithm (AOA), Grey Wolf Optimization (GWO), GSA, and GA, respectively.

Keywords
Active and reactive power controller, Grid-connected PV system, Gravitational Search Algorithm (GSA), Genetic Algorithm (GA), SOA and BFOA.

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