Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network

Yasen A. Harrye; Khaled H. Ahmed; Ahmed A. Aboushady

doi:10.1109/ICRERA.2014.7016448

Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network

Yasen A. Harrye^*, Khaled H. Ahmed, Ahmed A. Aboushady

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

26 Citations (Scopus)

Abstract

Reactive power flow increases dual active bridge (DAB) converter RMS current leading to an increase in conduction losses especially in high power applications. This paper proposes a new optimized triple phase shift (TPS) switching algorithm that minimizes the total reactive power of the converter. The algorithm iteratively searches for TPS control variables that satisfy the desired active power flow while selecting the operating mode with minimum reactive power consumption. This is valid for the whole range of converter operation. The iterative algorithm is run offline for the entire active power range (-1pu to 1pu) and the resulting data is used to train an open loop artificial neural network controller to reduce computational time and memory allocation necessary to store the data generated. To validate the accuracy of the proposed controller, a 500-MW 300kV/100kV DAB model is simulated in Matlab/Simulink, as a potential application for DAB in DC grids.

Original language	English
Title of host publication	3rd International Conference on Renewable Energy Research and Applications (ICRERA 2014)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	566-571
Number of pages	6
ISBN (Electronic)	9781479937950
ISBN (Print)	9781479937967
DOIs	https://doi.org/10.1109/ICRERA.2014.7016448
Publication status	Published - Oct 2014
Event	3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014 - Milwaukee, United States Duration: 19 Oct 2014 → 22 Oct 2014

Conference

Conference	3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014
Country/Territory	United States
City	Milwaukee
Period	19/10/14 → 22/10/14

Keywords

DC-DC converter
Dual active bridge (DAB) converter
Neural network (NN) controller
Reactive power
Triple phase shift (TPS)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

Harrye, Y. A., Ahmed, K. H., & Aboushady, A. A. (2014). Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network. In 3rd International Conference on Renewable Energy Research and Applications (ICRERA 2014) (pp. 566-571). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRERA.2014.7016448

Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network. / Harrye, Yasen A.; Ahmed, Khaled H.; Aboushady, Ahmed A.

3rd International Conference on Renewable Energy Research and Applications (ICRERA 2014). Institute of Electrical and Electronics Engineers Inc., 2014. p. 566-571.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Harrye, YA, Ahmed, KH & Aboushady, AA 2014, Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network. in 3rd International Conference on Renewable Energy Research and Applications (ICRERA 2014). Institute of Electrical and Electronics Engineers Inc., pp. 566-571, 3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014, Milwaukee, United States, 19/10/14. https://doi.org/10.1109/ICRERA.2014.7016448

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title = "Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network",

abstract = "Reactive power flow increases dual active bridge (DAB) converter RMS current leading to an increase in conduction losses especially in high power applications. This paper proposes a new optimized triple phase shift (TPS) switching algorithm that minimizes the total reactive power of the converter. The algorithm iteratively searches for TPS control variables that satisfy the desired active power flow while selecting the operating mode with minimum reactive power consumption. This is valid for the whole range of converter operation. The iterative algorithm is run offline for the entire active power range (-1pu to 1pu) and the resulting data is used to train an open loop artificial neural network controller to reduce computational time and memory allocation necessary to store the data generated. To validate the accuracy of the proposed controller, a 500-MW 300kV/100kV DAB model is simulated in Matlab/Simulink, as a potential application for DAB in DC grids.",

keywords = "DC-DC converter, Dual active bridge (DAB) converter, Neural network (NN) controller, Reactive power, Triple phase shift (TPS)",

author = "Harrye, {Yasen A.} and Ahmed, {Khaled H.} and Aboushady, {Ahmed A.}",

year = "2014",

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doi = "10.1109/ICRERA.2014.7016448",

language = "English",

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booktitle = "3rd International Conference on Renewable Energy Research and Applications (ICRERA 2014)",

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AU - Harrye, Yasen A.

AU - Ahmed, Khaled H.

AU - Aboushady, Ahmed A.

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N2 - Reactive power flow increases dual active bridge (DAB) converter RMS current leading to an increase in conduction losses especially in high power applications. This paper proposes a new optimized triple phase shift (TPS) switching algorithm that minimizes the total reactive power of the converter. The algorithm iteratively searches for TPS control variables that satisfy the desired active power flow while selecting the operating mode with minimum reactive power consumption. This is valid for the whole range of converter operation. The iterative algorithm is run offline for the entire active power range (-1pu to 1pu) and the resulting data is used to train an open loop artificial neural network controller to reduce computational time and memory allocation necessary to store the data generated. To validate the accuracy of the proposed controller, a 500-MW 300kV/100kV DAB model is simulated in Matlab/Simulink, as a potential application for DAB in DC grids.

AB - Reactive power flow increases dual active bridge (DAB) converter RMS current leading to an increase in conduction losses especially in high power applications. This paper proposes a new optimized triple phase shift (TPS) switching algorithm that minimizes the total reactive power of the converter. The algorithm iteratively searches for TPS control variables that satisfy the desired active power flow while selecting the operating mode with minimum reactive power consumption. This is valid for the whole range of converter operation. The iterative algorithm is run offline for the entire active power range (-1pu to 1pu) and the resulting data is used to train an open loop artificial neural network controller to reduce computational time and memory allocation necessary to store the data generated. To validate the accuracy of the proposed controller, a 500-MW 300kV/100kV DAB model is simulated in Matlab/Simulink, as a potential application for DAB in DC grids.

KW - DC-DC converter

KW - Dual active bridge (DAB) converter

KW - Neural network (NN) controller

KW - Reactive power

KW - Triple phase shift (TPS)

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BT - 3rd International Conference on Renewable Energy Research and Applications (ICRERA 2014)

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Y2 - 19 October 2014 through 22 October 2014

ER -

Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network

Abstract

Conference

Keywords

UN SDGs

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