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Automatika : journal for control, measurement, electronics, computing and communications, Vol. 65 No. 3, 2024.

Original scientific paper

https://doi.org/10.1080/00051144.2024.2329494

Renewable energy resource integrated multilevel inverter using evolutionary algorithms

B. Gopinath ; Department of Electrical and Electronics Engineering, Christ the King Engineering College, Coimbatore, India *
S. Suresh ; Department of Electrical and Electronics Engineering, Sri Eshwar College of Engineering, Coimbatore, India
G. Jayabaskaran ; Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, India
M. Geetha ; Department of Electrical and Electronics Engineering, Sri Eshwar College of Engineering, Coimbatore, India

* Corresponding author.

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Abstract

In this paper, with the development of an intelligent power system idea, sustainable energy
sources were increasingly deployed, including transmission and distribution systems networks.
As a result, optimal use of cascaded H-bridge inverter topologies (MLIs) and power distribution operations is critical for long-term power generation. Traditionally, selective harmonics
reduction models must be performed to achieve the optimal switching frequency of multilevel
inverters. This research aims to determine the switching frequency for wind-incorporated multilevel inverters to reduce overall harmonic components used in grid applications. This research
adds towards the best possible solution by employing multiple newly established adaptive optimization techniques: MNSGA-II and salp swarm. The well-known genetic algorithm and particle
swarm optimization are used for the wind-tied multilevel inverters optimization issue. Sevenlevel, eleven-level, and fifteen-level MLIs were employed to reduce overall harmonic distortion.
The reliability and convergence rate of simulated data with various modulation indices for seven-
, eleven-, and fifteen-level MLIs are obtained and compared. Models are developed based on
MATLAB Simulink and are used to validate quantitative measurements.

Keywords

Modified non-dominated sorting genetic algorithm (MNSGA-II); salp swarm algorithm (SSA); particle swarm optimization (PSO); multilevel inverters (MLIs); modulation index; cascaded H-bridge

Hrčak ID:

326257

URI

https://hrcak.srce.hr/326257

Publication date:

26.3.2024.

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