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Real-Time Selective Harmonic Mitigation Technique for Power Converters Based on the Exchange Market Algorithm

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submitted on 2024-05-20, 07:36 and posted on 2024-05-20, 07:36 authored by Abraham Marquez Alcaide, Jose I. Leon, Marta Laguna, Francisco Gonzalez-Rodriguez, Ramon Portillo, Eduardo Zafra-Ratia, Sergio Vazquez, Leopoldo G. Franquelo, Sertac Bayhan, Haitham Abu-Rub

Hand-in-hand with the smart-grid paradigm development, power converters used in high-power applications are facing important challenges related to efficiency and power quality. To overcome these issues, the pre-programmed Pulse-Width Modulation (PWM) methods have been extensively applied to reduce the harmonic distortion with very low power switching losses for high-power converters. Among the pre-programmed PWM techniques, Selective Harmonic Elimination (SHE) has been the prevailing solution, but recently, Selective Harmonic Mitigation (SHM) stands as a superior alternative to provide further control of the harmonic spectrum with similar losses. However, the large computational burden required by the SHM method to find a solution confines it as an off-line application, where the switching set valid solutions are pre-computed and stored in a memory. In this paper, for the first time, a real-time implementation of SHM using an off-the-shelf mid-range microcontroller is presented and tested. The Exchange Market Algorithm (EMA), initially focused on optimizing financial transactions, is considered and executed to achieve the SHM targets. The performance of the EMA-based SHM is presented showing experimental results considering a reduced number of switching angles applied to a specific three-level converter, but the method can be extrapolated to any other three-level converter topology.

Other Information

Published in: Energies
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.3390/en13071659

Additional institutions affiliated with: Smart Grid Center in Qatar - TAMUQ

Funding

Microgrids Advanced Dynamic Control Architecture and Distributed Energy Optimization - https://app.dimensions.ai/details/grant/grant.5302746

History

Language

  • English

Publisher

MDPI

Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Environment and Energy Research Institute - HBKU
  • Texas A&M University at Qatar
  • Texas A&M Engineering Experiment Station - TAMUQ

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