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

Yasen A. Harrye, Khaled Ahmed, Ahmed Aboushady

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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 languageEnglish
Title of host publication2014 International Conference on Renewable Energy Research and Application (ICRERA)
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-4799-3795-0
DOIs
Publication statusPublished - 2014

Fingerprint

DC-DC converters
Reactive power
Phase shift
Neural networks
Storage allocation (computer)
Controllers
Electric power utilization

Keywords

  • reactive power
  • circuit breakers
  • artificial neural networks
  • optimization
  • bridge circuits
  • power conversion

Cite this

Harrye, Y. A., Ahmed, K., & Aboushady, A. (2014). Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network. In 2014 International Conference on Renewable Energy Research and Application (ICRERA) IEEE. https://doi.org/10.1109/ICRERA.2014.7016448
Harrye, Yasen A. ; Ahmed, Khaled ; Aboushady, Ahmed. / Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network. 2014 International Conference on Renewable Energy Research and Application (ICRERA). IEEE, 2014.
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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.",
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Harrye, YA, Ahmed, K & Aboushady, A 2014, Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network. in 2014 International Conference on Renewable Energy Research and Application (ICRERA). IEEE. 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; Aboushady, Ahmed.

2014 International Conference on Renewable Energy Research and Application (ICRERA). IEEE, 2014.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network

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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.

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Harrye YA, Ahmed K, Aboushady A. Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network. In 2014 International Conference on Renewable Energy Research and Application (ICRERA). IEEE. 2014 https://doi.org/10.1109/ICRERA.2014.7016448