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

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Artificial Neural Network and Newton Raphson (ANN-NR) Algorithm Based Selective Harmonic Elimination in Cascaded Multilevel Inverter for PV Applications. / Padmanaban, Sanjeevikumar; Dhanamjayulu, C.; Khan, Baseem.

In: IEEE Access, Vol. 9, 02.2021, p. 75058-75070.

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

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Padmanaban, Sanjeevikumar ; Dhanamjayulu, C. ; Khan, Baseem. / Artificial Neural Network and Newton Raphson (ANN-NR) Algorithm Based Selective Harmonic Elimination in Cascaded Multilevel Inverter for PV Applications. In: IEEE Access. 2021 ; Vol. 9. pp. 75058-75070.

Bibtex

@article{f72e09b50dd04b6cbf2bbe06f1cbc234,
title = "Artificial Neural Network and Newton Raphson (ANN-NR) Algorithm Based Selective Harmonic Elimination in Cascaded Multilevel Inverter for PV Applications",
abstract = "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.",
keywords = "Artificial neural network (ANN), Maximum Power Point Tracking (MPPT), Newton Raphson (NR) method, Perturb & Observe (P&O), Selective Harmonic Elimination (SHE) PWM",
author = "Sanjeevikumar Padmanaban and C. Dhanamjayulu and Baseem Khan",
note = "Publisher Copyright: {\textcopyright} 2013 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = feb,
doi = "10.1109/ACCESS.2021.3081460",
language = "English",
volume = "9",
pages = "75058--75070",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers",

}

RIS

TY - JOUR

T1 - Artificial Neural Network and Newton Raphson (ANN-NR) Algorithm Based Selective Harmonic Elimination in Cascaded Multilevel Inverter for PV Applications

AU - Padmanaban, Sanjeevikumar

AU - Dhanamjayulu, C.

AU - Khan, Baseem

N1 - Publisher Copyright: © 2013 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/2

Y1 - 2021/2

N2 - 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.

AB - 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.

KW - Artificial neural network (ANN)

KW - Maximum Power Point Tracking (MPPT)

KW - Newton Raphson (NR) method

KW - Perturb & Observe (P&O)

KW - Selective Harmonic Elimination (SHE) PWM

UR - http://www.scopus.com/inward/record.url?scp=85107195259&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2021.3081460

DO - 10.1109/ACCESS.2021.3081460

M3 - Journal article

AN - SCOPUS:85107195259

VL - 9

SP - 75058

EP - 75070

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -