A Distortion-Based Potential Game for Secondary Voltage Control in Micro-Grids

In this work, we present a multi-agent learning model based on the maximum entropy (MAXEnt) and the rate distortion function to define, respectively, the environment of the agents and their understanding about it. The avoidance of redundant information under distortion conditions is used to define a...
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Autor*in:	David Alejandro Martinez [verfasserIn] Eduardo Mojica-Nava [verfasserIn] Ameena Saad Al-Sumaiti [verfasserIn] Sergio Rivera [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Maximum entropy micro-grids potential games rate distortion function

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 110611-110622
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:110611-110622

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3002713

Katalog-ID:	DOAJ013570420

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allfieldsGer	10.1109/ACCESS.2020.3002713 doi (DE-627)DOAJ013570420 (DE-599)DOAJ5e58b885cb074c3ba781d04cb9ab18eb DE-627 ger DE-627 rakwb eng TK1-9971 David Alejandro Martinez verfasserin aut A Distortion-Based Potential Game for Secondary Voltage Control in Micro-Grids 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, we present a multi-agent learning model based on the maximum entropy (MAXEnt) and the rate distortion function to define, respectively, the environment of the agents and their understanding about it. The avoidance of redundant information under distortion conditions is used to define a distortion-based potential function that is minimized in order to find an equilibrium point in a potential game setting, in which the Lagrange multiplier β, used as input in the Blahut-Arimoto algorithm, determines the rationality in the learning process. The model performance is evaluated in a secondary voltage controller in order to achieve reactive power sharing between distributed generators (DGs) in a micro-grid. Simulation results demonstrate a good response in terms of reactive power distribution when the load is increased in a DG without considerable affectations in the voltage stability. Maximum entropy micro-grids potential games rate distortion function Electrical engineering. Electronics. Nuclear engineering Eduardo Mojica-Nava verfasserin aut Ameena Saad Al-Sumaiti verfasserin aut Sergio Rivera verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 110611-110622 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:110611-110622 https://doi.org/10.1109/ACCESS.2020.3002713 kostenfrei https://doaj.org/article/5e58b885cb074c3ba781d04cb9ab18eb kostenfrei https://ieeexplore.ieee.org/document/9117110/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 110611-110622
allfieldsSound	10.1109/ACCESS.2020.3002713 doi (DE-627)DOAJ013570420 (DE-599)DOAJ5e58b885cb074c3ba781d04cb9ab18eb DE-627 ger DE-627 rakwb eng TK1-9971 David Alejandro Martinez verfasserin aut A Distortion-Based Potential Game for Secondary Voltage Control in Micro-Grids 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, we present a multi-agent learning model based on the maximum entropy (MAXEnt) and the rate distortion function to define, respectively, the environment of the agents and their understanding about it. The avoidance of redundant information under distortion conditions is used to define a distortion-based potential function that is minimized in order to find an equilibrium point in a potential game setting, in which the Lagrange multiplier β, used as input in the Blahut-Arimoto algorithm, determines the rationality in the learning process. The model performance is evaluated in a secondary voltage controller in order to achieve reactive power sharing between distributed generators (DGs) in a micro-grid. Simulation results demonstrate a good response in terms of reactive power distribution when the load is increased in a DG without considerable affectations in the voltage stability. Maximum entropy micro-grids potential games rate distortion function Electrical engineering. Electronics. Nuclear engineering Eduardo Mojica-Nava verfasserin aut Ameena Saad Al-Sumaiti verfasserin aut Sergio Rivera verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 110611-110622 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:110611-110622 https://doi.org/10.1109/ACCESS.2020.3002713 kostenfrei https://doaj.org/article/5e58b885cb074c3ba781d04cb9ab18eb kostenfrei https://ieeexplore.ieee.org/document/9117110/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 110611-110622
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callnumber-first	T - Technology
author	David Alejandro Martinez
spellingShingle	David Alejandro Martinez misc TK1-9971 misc Maximum entropy misc micro-grids misc potential games misc rate distortion function misc Electrical engineering. Electronics. Nuclear engineering A Distortion-Based Potential Game for Secondary Voltage Control in Micro-Grids
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topic_title	TK1-9971 A Distortion-Based Potential Game for Secondary Voltage Control in Micro-Grids Maximum entropy micro-grids potential games rate distortion function
topic	misc TK1-9971 misc Maximum entropy misc micro-grids misc potential games misc rate distortion function misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Maximum entropy misc micro-grids misc potential games misc rate distortion function misc Electrical engineering. Electronics. Nuclear engineering
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title	A Distortion-Based Potential Game for Secondary Voltage Control in Micro-Grids
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title_sort	distortion-based potential game for secondary voltage control in micro-grids
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title_auth	A Distortion-Based Potential Game for Secondary Voltage Control in Micro-Grids
abstract	In this work, we present a multi-agent learning model based on the maximum entropy (MAXEnt) and the rate distortion function to define, respectively, the environment of the agents and their understanding about it. The avoidance of redundant information under distortion conditions is used to define a distortion-based potential function that is minimized in order to find an equilibrium point in a potential game setting, in which the Lagrange multiplier β, used as input in the Blahut-Arimoto algorithm, determines the rationality in the learning process. The model performance is evaluated in a secondary voltage controller in order to achieve reactive power sharing between distributed generators (DGs) in a micro-grid. Simulation results demonstrate a good response in terms of reactive power distribution when the load is increased in a DG without considerable affectations in the voltage stability.
abstractGer	In this work, we present a multi-agent learning model based on the maximum entropy (MAXEnt) and the rate distortion function to define, respectively, the environment of the agents and their understanding about it. The avoidance of redundant information under distortion conditions is used to define a distortion-based potential function that is minimized in order to find an equilibrium point in a potential game setting, in which the Lagrange multiplier β, used as input in the Blahut-Arimoto algorithm, determines the rationality in the learning process. The model performance is evaluated in a secondary voltage controller in order to achieve reactive power sharing between distributed generators (DGs) in a micro-grid. Simulation results demonstrate a good response in terms of reactive power distribution when the load is increased in a DG without considerable affectations in the voltage stability.
abstract_unstemmed	In this work, we present a multi-agent learning model based on the maximum entropy (MAXEnt) and the rate distortion function to define, respectively, the environment of the agents and their understanding about it. The avoidance of redundant information under distortion conditions is used to define a distortion-based potential function that is minimized in order to find an equilibrium point in a potential game setting, in which the Lagrange multiplier β, used as input in the Blahut-Arimoto algorithm, determines the rationality in the learning process. The model performance is evaluated in a secondary voltage controller in order to achieve reactive power sharing between distributed generators (DGs) in a micro-grid. Simulation results demonstrate a good response in terms of reactive power distribution when the load is increased in a DG without considerable affectations in the voltage stability.
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title_short	A Distortion-Based Potential Game for Secondary Voltage Control in Micro-Grids
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