A neural network-based selective harmonic elimination scheme for five-level inverter
This paper presents the implementation of selective harmonic elimination (SHE) in a five-level inverter structure using artificial neural networks (ANNs). SHE is an effective low-frequency modulation technique to eliminate selected harmonics and control multilevel converters. The use of ANN-SHE requ...
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my.um.eprints.337812022-04-27T02:30:24Z http://eprints.um.edu.my/33781/ A neural network-based selective harmonic elimination scheme for five-level inverter Maamar, Alla Eddine Toubal Helaimi, M'hamed Taleb, Rachid Kermadi, Mostefa Mekhilef, Saad TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering This paper presents the implementation of selective harmonic elimination (SHE) in a five-level inverter structure using artificial neural networks (ANNs). SHE is an effective low-frequency modulation technique to eliminate selected harmonics and control multilevel converters. The use of ANN-SHE requires the calculation of the optimum values of switching angles via the solving system of nonlinear equations for the total harmonic distortion (THD) reduction, where the nonlinear equations are founded by the complex Fourier series analysis of the inverter output voltage. The procured switching angle values are directly implemented by a multilayer perceptron (MLP) algorithm without a lookup table. The ANN model is obtained by training the neural network (NN), taking the modulation index (M) as an input and approximating switching angles as an output. A thorough analysis was carried out to show the programming steps of the proposed ANN-based SHE using Matlab/Simulink environment. A realized inverter prototype steered by the proposed ANN-based SHE was tested with various modulation indexes on a real-time mode using a digital signal processor (DSP) C2000 Delfino-TMS320F28379D-embedded board. A comparison between the simulation results and the experimental data is presented. The obtained results illustrate that the experimental results match the simulation closely, and the ANN model provides a fast and precise estimate of the switching angles for each value of the modulation index. John Wiley & Sons 2022-01 Article PeerReviewed Maamar, Alla Eddine Toubal and Helaimi, M'hamed and Taleb, Rachid and Kermadi, Mostefa and Mekhilef, Saad (2022) A neural network-based selective harmonic elimination scheme for five-level inverter. International Journal of Circuit Theory and Applications, 50 (1). pp. 298-316. ISSN 0098-9886, DOI https://doi.org/10.1002/cta.3130 <https://doi.org/10.1002/cta.3130>. 10.1002/cta.3130 |
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TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Maamar, Alla Eddine Toubal Helaimi, M'hamed Taleb, Rachid Kermadi, Mostefa Mekhilef, Saad A neural network-based selective harmonic elimination scheme for five-level inverter |
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This paper presents the implementation of selective harmonic elimination (SHE) in a five-level inverter structure using artificial neural networks (ANNs). SHE is an effective low-frequency modulation technique to eliminate selected harmonics and control multilevel converters. The use of ANN-SHE requires the calculation of the optimum values of switching angles via the solving system of nonlinear equations for the total harmonic distortion (THD) reduction, where the nonlinear equations are founded by the complex Fourier series analysis of the inverter output voltage. The procured switching angle values are directly implemented by a multilayer perceptron (MLP) algorithm without a lookup table. The ANN model is obtained by training the neural network (NN), taking the modulation index (M) as an input and approximating switching angles as an output. A thorough analysis was carried out to show the programming steps of the proposed ANN-based SHE using Matlab/Simulink environment. A realized inverter prototype steered by the proposed ANN-based SHE was tested with various modulation indexes on a real-time mode using a digital signal processor (DSP) C2000 Delfino-TMS320F28379D-embedded board. A comparison between the simulation results and the experimental data is presented. The obtained results illustrate that the experimental results match the simulation closely, and the ANN model provides a fast and precise estimate of the switching angles for each value of the modulation index. |
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Article |
author |
Maamar, Alla Eddine Toubal Helaimi, M'hamed Taleb, Rachid Kermadi, Mostefa Mekhilef, Saad |
author_facet |
Maamar, Alla Eddine Toubal Helaimi, M'hamed Taleb, Rachid Kermadi, Mostefa Mekhilef, Saad |
author_sort |
Maamar, Alla Eddine Toubal |
title |
A neural network-based selective harmonic elimination scheme for five-level inverter |
title_short |
A neural network-based selective harmonic elimination scheme for five-level inverter |
title_full |
A neural network-based selective harmonic elimination scheme for five-level inverter |
title_fullStr |
A neural network-based selective harmonic elimination scheme for five-level inverter |
title_full_unstemmed |
A neural network-based selective harmonic elimination scheme for five-level inverter |
title_sort |
neural network-based selective harmonic elimination scheme for five-level inverter |
publisher |
John Wiley & Sons |
publishDate |
2022 |
url |
http://eprints.um.edu.my/33781/ |
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1735409590648963072 |