Improved Genetic Algorithm-Based Harmonic Mitigation Control of an Asymmetrical Dual-Source 13-Level Switched-Capacitor Multilevel Inverter

Hasan Iqbal () and Arif Sarwat ()
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Hasan Iqbal: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Arif Sarwat: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA

Energies, 2024, vol. 18, issue 1, 1-24

Abstract: A single-phase multilevel inverter with a switched-capacitor multilevel (SC-MLI) configuration is developed to provide 13-level output voltages. An improved genetic algorithm (GA) with adaptive mutation and crossover rates is employed to achieve robust harmonic mitigation by avoiding local optima and ensuring optimal performance. The topology introduces an SC-MLI that generates AC output voltage at desired levels using only two capacitors, two asymmetrical DC sources, one diode, and 11 switches. This allows the inverter to use fewer gate drivers and, hence, increases the power density of the converter. A significant challenge in the normal operation of SC-MLI circuits relates to the self-voltage balance of the capacitors, which easily becomes unstable, particularly at low modulation indices. The proposed design addresses this issue without the need for ancillary devices or complex control schemes, ensuring stable self-balanced operation across the entire spectrum of the modulation index. In this context, the harmonic mitigation technique optimized through GA applied in this inverter ensures low harmonic distortion, achieving a total harmonic distortion (THD) of 6.73%, thereby enhancing power quality even at low modulation indices. The performance of this SC-MLI is modeled under various loading scenarios using MATLAB/Simulink ® 2023b with validation performed through an Opal-RT real-time emulator. Additionally, the inverter’s overall power losses and individual switch losses, along with the efficiency, are analyzed using the simulation tool PLEXIM-PLECS. Efficiency is found to be 96.62%.

Keywords: renewable energy; SC-MLI; power quality; THD; GA; power loss analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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