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Genetic Algorithm Based 7-Level Step-Up Inverter with Reduced Harmonics and Switching Devices

T. Anand Kumar1,*, M. Kaliamoorthy1, I. Gerald Christopher Raj2

1 Department of EEE, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India
2 Department of EEE, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India

* Corresponding Author: T. Anand Kumar. Email: email

Intelligent Automation & Soft Computing 2023, 35(3), 3081-3097.


This paper presents a unique voltage-raising topology for a single-phase seven-level inverter with triple output voltage gain using single input source and two switched capacitors. The output voltage has been boosted up to three times the value of input voltage by configuring the switched capacitors in series and parallel combinations which eliminates the use of additional step-up converters and transformers. The selective harmonic elimination (SHE) approach is used to remove the lower-order harmonics. The optimal switching angles for SHE is determined using the genetic algorithm. These switching angles are combined with a level-shifted pulse width modulation (PWM) technique for pulse generation, resulting in reduced total harmonic distortion (THD). A detailed comparison has been made against other relevant seven-level inverter topologies in terms of the number of switches, drivers, diodes, capacitors, and boosting facilities to emphasize the benefits of the proposed model. The proposed topology is simulated using MATLAB/SIMULINK and an experimental prototype has been developed to validate the results. The Digital Signal Processing (DSP) TMS320F2812 board is used to generate the switching pulses for the proposed technique and the experimental results concur with the simulated model outputs.


Cite This Article

T. Anand Kumar, M. Kaliamoorthy and I. Gerald Christopher Raj, "Genetic algorithm based 7-level step-up inverter with reduced harmonics and switching devices," Intelligent Automation & Soft Computing, vol. 35, no.3, pp. 3081–3097, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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