Department of Business Development and Technology

Artificial Neural Network and Newton Raphson (ANN-NR) Algorithm Based Selective Harmonic Elimination in Cascaded Multilevel Inverter for PV Applications

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Sanjeevikumar Padmanaban
  • ,
  • C. Dhanamjayulu, Vellore Institute of Technology
  • ,
  • Baseem Khan, Hawassa University

In this article, a hybrid Artificial Neural Network-Newton Raphson (ANN-NR) is introduced to mitigate the undesired lower-order harmonic content in the cascaded H-Bridge multilevel inverter for solar photovoltaic (PV). Harmonics are extracted by the excellent choice of opting switching angles by exploiting the Selective Harmonic Elimination (SHE) PWM technique accompanying a unified algorithm in order to optimize and reduce the Total Harmonic Distortion (THD). ANN is trained with optimum switching angles, and the estimates generated by the ANN are the initial guess for NR. In this study, the CHB-MLI is combined with a traditional boost converter, it boosts the PV voltage to a superior dc-link voltage Perturb and Observe (PO) based Maximum Power Point Tracking (MPPT) algorithm is used for getting a stable output and efficient operation of solar PV. The proposed system is proved over an eleven-level H-bridge inverter, the work is carried out in MATLAB/Simulink environment, and the respective results are confirmed that the proposed technique is efficient, and offers an actual firing angles with a few iterations results in a better capability of confronting local optima values. The suggested algorithm is justified by the experimental development of eleven-level cascaded H-bridge inverter.

Original languageEnglish
JournalIEEE Access
Pages (from-to)75058-75070
Number of pages13
Publication statusPublished - Feb 2021

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Copyright 2021 Elsevier B.V., All rights reserved.

    Research areas

  • Artificial neural network (ANN), Maximum Power Point Tracking (MPPT), Newton Raphson (NR) method, Perturb & Observe (P&O), Selective Harmonic Elimination (SHE) PWM

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