Predictive Voltage Control at Fixed Switching Frequency with Reduced Reactive Power in a Direct Matrix Converter

Rojas, Diego; Rivera, Marco; Toledo, Sergio; Wheeler, Patrick

Abstract

Classical predictive control implementations generate variable switching frequency which could produce input filter resonances in matrix converters topologies, affecting system performance. This paper considers the application of predictive control to a Direct Matrix Converter (DMC) in order to improve the power factor based on a modulated model with a fixed switching frequency (M2PC). The M2PC technique allows control of the output waveforms of the DMC at the same time as controlling the input current and reactive power. The discrete time model of the system as well as the input and output filter parameters are used to predict the behaviour of supply side reactive power and output waveforms for each valid switching state of the DMC. The simulation results from Matlab/Simulink are used to confirm the optimal performance of this strategy.

Citation

Rojas, D., Rivera, M., Toledo, S., & Wheeler, P. (2021, March). Predictive Voltage Control at Fixed Switching Frequency with Reduced Reactive Power in a Direct Matrix Converter. Presented at IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA 2021), Valparaíso, Chile

• Presentation Conference Type: Edited Proceedings
• Conference Name: IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA 2021)
• Start Date: Mar 22, 2021
• End Date: Mar 26, 2021
• Acceptance Date: Jan 24, 2021
• Online Publication Date: Jul 1, 2021
• Publication Date: Mar 22, 2021
• Deposit Date: Oct 29, 2021
• Publicly Available Date: Oct 29, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 556-561
• Book Title: 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA)
• ISBN: 9781665429788
• DOI: https://doi.org/10.1109/icaacca51523.2021.9465334
• Public URL: https://nottingham-repository.worktribe.com/output/6544094
• Publisher URL: https://ieeexplore.ieee.org/document/9465334
• Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.