DC/DC Power Converter Control-Based Deep Machine Learning Techniques: Real-Time Implementation

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DC/DC Power Converter Control-Based Deep Machine Learning Techniques : Real-Time Implementation . / Hajihosseini, Mojtaba ; Andalibi, Milad ; Gheisarnejad, Meysam; Farsizadeh, Hamed; Khooban, Mohammad Hassan.

I: IEEE Transactions on Power Electronics, Bind 35, Nr. 10, 10.2020, s. 9971-9977.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Hajihosseini, M, Andalibi, M, Gheisarnejad, M, Farsizadeh, H & Khooban, MH 2020, 'DC/DC Power Converter Control-Based Deep Machine Learning Techniques: Real-Time Implementation ', IEEE Transactions on Power Electronics, bind 35, nr. 10, s. 9971-9977. https://doi.org/10.1109/TPEL.2020.2977765

APA

Hajihosseini, M., Andalibi, M., Gheisarnejad, M., Farsizadeh, H., & Khooban, M. H. (2020). DC/DC Power Converter Control-Based Deep Machine Learning Techniques: Real-Time Implementation . IEEE Transactions on Power Electronics, 35(10), 9971-9977. https://doi.org/10.1109/TPEL.2020.2977765

CBE

Hajihosseini M, Andalibi M, Gheisarnejad M, Farsizadeh H, Khooban MH. 2020. DC/DC Power Converter Control-Based Deep Machine Learning Techniques: Real-Time Implementation . IEEE Transactions on Power Electronics. 35(10):9971-9977. https://doi.org/10.1109/TPEL.2020.2977765

MLA

Hajihosseini, Mojtaba o.a.. "DC/DC Power Converter Control-Based Deep Machine Learning Techniques: Real-Time Implementation ". IEEE Transactions on Power Electronics. 2020, 35(10). 9971-9977. https://doi.org/10.1109/TPEL.2020.2977765

Vancouver

Hajihosseini M, Andalibi M, Gheisarnejad M, Farsizadeh H, Khooban MH. DC/DC Power Converter Control-Based Deep Machine Learning Techniques: Real-Time Implementation . IEEE Transactions on Power Electronics. 2020 okt;35(10):9971-9977. https://doi.org/10.1109/TPEL.2020.2977765

Author

Hajihosseini, Mojtaba ; Andalibi, Milad ; Gheisarnejad, Meysam ; Farsizadeh, Hamed ; Khooban, Mohammad Hassan. / DC/DC Power Converter Control-Based Deep Machine Learning Techniques : Real-Time Implementation . I: IEEE Transactions on Power Electronics. 2020 ; Bind 35, Nr. 10. s. 9971-9977.

Bibtex

@article{0c891ceb88d04a64baf8d7a32d4998b7,
title = "DC/DC Power Converter Control-Based Deep Machine Learning Techniques: Real-Time Implementation ",
abstract = "The recent advances in power plants and energy resources have extended the applications of buck-boost converters in the context of DC micro-grids (MGs). However, the implementation of such interface systems in the MG applications is seriously threatened with instability issues imposed by the constant power loads (CPLs). The objective is that without the accurate modeling information of a DC MG system, develop a new adaptive control methodology for voltage stabilization of the DC-DC converters feeding CPLs with low ripples. To achieve this goal, in this letter, the deep reinforcement learning (DRL) technique with the Actor-Critic architecture is incorporated into an ultra-local model (ULM) control scheme to address the de-stabilization effect of the CPLs under the reference voltage variations. In the suggested control approach, the feedback controller gains of the ULM controller are considered as the adjustable controller coefficients which will be adaptively designed by the DRL technique through online learning of its neural networks (NNs). It is proved that the suggested scheme will ensure the rigorous stability of the power electronic system, for simultaneous effects of CPL and reference voltage changes, by adaptively adjusting the ULM controller gains. To appraise the merits and usefulness of the suggested adaptive methodology, some dSPACE MicroLabBox outcomes on a real-time testbed of the DC-DC converter feeding a CPL are presented.",
author = "Mojtaba Hajihosseini and Milad Andalibi and Meysam Gheisarnejad and Hamed Farsizadeh and Khooban, {Mohammad Hassan}",
year = "2020",
month = oct,
doi = "10.1109/TPEL.2020.2977765",
language = "English",
volume = "35",
pages = "9971--9977",
journal = "IEEE Transactions on Power Electronics",
issn = "0885-8993",
publisher = "Institute of Electrical and Electronics Engineers",
number = "10",

}

RIS

TY - JOUR

T1 - DC/DC Power Converter Control-Based Deep Machine Learning Techniques

T2 - Real-Time Implementation

AU - Hajihosseini, Mojtaba

AU - Andalibi, Milad

AU - Gheisarnejad, Meysam

AU - Farsizadeh, Hamed

AU - Khooban, Mohammad Hassan

PY - 2020/10

Y1 - 2020/10

N2 - The recent advances in power plants and energy resources have extended the applications of buck-boost converters in the context of DC micro-grids (MGs). However, the implementation of such interface systems in the MG applications is seriously threatened with instability issues imposed by the constant power loads (CPLs). The objective is that without the accurate modeling information of a DC MG system, develop a new adaptive control methodology for voltage stabilization of the DC-DC converters feeding CPLs with low ripples. To achieve this goal, in this letter, the deep reinforcement learning (DRL) technique with the Actor-Critic architecture is incorporated into an ultra-local model (ULM) control scheme to address the de-stabilization effect of the CPLs under the reference voltage variations. In the suggested control approach, the feedback controller gains of the ULM controller are considered as the adjustable controller coefficients which will be adaptively designed by the DRL technique through online learning of its neural networks (NNs). It is proved that the suggested scheme will ensure the rigorous stability of the power electronic system, for simultaneous effects of CPL and reference voltage changes, by adaptively adjusting the ULM controller gains. To appraise the merits and usefulness of the suggested adaptive methodology, some dSPACE MicroLabBox outcomes on a real-time testbed of the DC-DC converter feeding a CPL are presented.

AB - The recent advances in power plants and energy resources have extended the applications of buck-boost converters in the context of DC micro-grids (MGs). However, the implementation of such interface systems in the MG applications is seriously threatened with instability issues imposed by the constant power loads (CPLs). The objective is that without the accurate modeling information of a DC MG system, develop a new adaptive control methodology for voltage stabilization of the DC-DC converters feeding CPLs with low ripples. To achieve this goal, in this letter, the deep reinforcement learning (DRL) technique with the Actor-Critic architecture is incorporated into an ultra-local model (ULM) control scheme to address the de-stabilization effect of the CPLs under the reference voltage variations. In the suggested control approach, the feedback controller gains of the ULM controller are considered as the adjustable controller coefficients which will be adaptively designed by the DRL technique through online learning of its neural networks (NNs). It is proved that the suggested scheme will ensure the rigorous stability of the power electronic system, for simultaneous effects of CPL and reference voltage changes, by adaptively adjusting the ULM controller gains. To appraise the merits and usefulness of the suggested adaptive methodology, some dSPACE MicroLabBox outcomes on a real-time testbed of the DC-DC converter feeding a CPL are presented.

U2 - 10.1109/TPEL.2020.2977765

DO - 10.1109/TPEL.2020.2977765

M3 - Journal article

VL - 35

SP - 9971

EP - 9977

JO - IEEE Transactions on Power Electronics

JF - IEEE Transactions on Power Electronics

SN - 0885-8993

IS - 10

ER -