Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis

In this paper, the problem of frequency regulation in the multi-area power systems with demand response, energy storage system (ESS) and renewable energy generators is studied. Dissimilarly to most studies in this field, the dynamics of all units in all areas are considered to be unknown. Furthermor...

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Autores principales:	Ayman A. Aly, Bassem F. Felemban, Ardashir Mohammadzadeh, Oscar Castillo, Andrzej Bartoszewicz
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	type-3 fuzzy systems restricted Boltzmann machine control systems frequency regulation linear matrix inequality Technology T
Acceso en línea:	https://doaj.org/article/3cea5215b0a241bf93884edb59b150f1
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id	oai:doaj.org-article:3cea5215b0a241bf93884edb59b150f1
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spelling	oai:doaj.org-article:3cea5215b0a241bf93884edb59b150f12021-11-25T17:28:52ZFrequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis10.3390/en142278011996-1073https://doaj.org/article/3cea5215b0a241bf93884edb59b150f12021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7801https://doaj.org/toc/1996-1073In this paper, the problem of frequency regulation in the multi-area power systems with demand response, energy storage system (ESS) and renewable energy generators is studied. Dissimilarly to most studies in this field, the dynamics of all units in all areas are considered to be unknown. Furthermore time-varying solar radiation, wind speed dynamics, multiple load changes, demand response (DR), and ESS are considered. A novel dynamic fractional-order model based on restricted Boltzmann machine (RBM) and deep learning contrastive divergence (CD) algorithm is presented for online identification. The controller is designed by the dynamic estimated model, error feedback controller and interval type-3 fuzzy logic compensator (IT3-FLC). The gains of error feedback controller and tuning rules of the estimated dynamic model are extracted through the fractional-order stability analysis by the linear matrix inequality (LMI) approach. The superiority of a schemed controller in contrast to the type-1 and type-2 FLCs is demonstrated in various conditions, such as time-varying wind speed, solar radiation, multiple load changes, and perturbed dynamics.Ayman A. AlyBassem F. FelembanArdashir MohammadzadehOscar CastilloAndrzej BartoszewiczMDPI AGarticletype-3 fuzzy systemsrestricted Boltzmann machinecontrol systemsfrequency regulationlinear matrix inequalityTechnologyTENEnergies, Vol 14, Iss 7801, p 7801 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	type-3 fuzzy systems restricted Boltzmann machine control systems frequency regulation linear matrix inequality Technology T
spellingShingle	type-3 fuzzy systems restricted Boltzmann machine control systems frequency regulation linear matrix inequality Technology T Ayman A. Aly Bassem F. Felemban Ardashir Mohammadzadeh Oscar Castillo Andrzej Bartoszewicz Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis
description	In this paper, the problem of frequency regulation in the multi-area power systems with demand response, energy storage system (ESS) and renewable energy generators is studied. Dissimilarly to most studies in this field, the dynamics of all units in all areas are considered to be unknown. Furthermore time-varying solar radiation, wind speed dynamics, multiple load changes, demand response (DR), and ESS are considered. A novel dynamic fractional-order model based on restricted Boltzmann machine (RBM) and deep learning contrastive divergence (CD) algorithm is presented for online identification. The controller is designed by the dynamic estimated model, error feedback controller and interval type-3 fuzzy logic compensator (IT3-FLC). The gains of error feedback controller and tuning rules of the estimated dynamic model are extracted through the fractional-order stability analysis by the linear matrix inequality (LMI) approach. The superiority of a schemed controller in contrast to the type-1 and type-2 FLCs is demonstrated in various conditions, such as time-varying wind speed, solar radiation, multiple load changes, and perturbed dynamics.
format	article
author	Ayman A. Aly Bassem F. Felemban Ardashir Mohammadzadeh Oscar Castillo Andrzej Bartoszewicz
author_facet	Ayman A. Aly Bassem F. Felemban Ardashir Mohammadzadeh Oscar Castillo Andrzej Bartoszewicz
author_sort	Ayman A. Aly
title	Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis
title_short	Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis
title_full	Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis
title_fullStr	Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis
title_full_unstemmed	Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis
title_sort	frequency regulation system: a deep learning identification, type-3 fuzzy control and lmi stability analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3cea5215b0a241bf93884edb59b150f1
work_keys_str_mv	AT aymanaaly frequencyregulationsystemadeeplearningidentificationtype3fuzzycontrolandlmistabilityanalysis AT bassemffelemban frequencyregulationsystemadeeplearningidentificationtype3fuzzycontrolandlmistabilityanalysis AT ardashirmohammadzadeh frequencyregulationsystemadeeplearningidentificationtype3fuzzycontrolandlmistabilityanalysis AT oscarcastillo frequencyregulationsystemadeeplearningidentificationtype3fuzzycontrolandlmistabilityanalysis AT andrzejbartoszewicz frequencyregulationsystemadeeplearningidentificationtype3fuzzycontrolandlmistabilityanalysis
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Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis

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