Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method

This work focuses on enhancing the performance of an Automatic Voltage Regulator (AVR) by providing a good transient response, adaptability to changing circumstances, and robustness. Its objective is centered on utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) to control the AVR system. T...
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Autor*in:	Maman Jimoh Lawal [verfasserIn] Suleiman Usman Hussein [verfasserIn] Bemdoo Saka [verfasserIn] Sadiq Umar Abubakar [verfasserIn] Idoko S. Attah [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Automatic voltage regulator ANFIS PID Fuzzy Control Power System

Übergeordnetes Werk:	In: Scientific African - Elsevier, 2018, 19(2023), Seite e01573-
Übergeordnetes Werk:	volume:19 ; year:2023 ; pages:e01573-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.sciaf.2023.e01573

Katalog-ID:	DOAJ080862454

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allfieldsGer	10.1016/j.sciaf.2023.e01573 doi (DE-627)DOAJ080862454 (DE-599)DOAJ2d31f2b25a2b4f8f9e46d8027828eb4f DE-627 ger DE-627 rakwb eng Maman Jimoh Lawal verfasserin aut Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work focuses on enhancing the performance of an Automatic Voltage Regulator (AVR) by providing a good transient response, adaptability to changing circumstances, and robustness. Its objective is centered on utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) to control the AVR system. The study is important because it compares the performance of the system without a controller, with the PID controller and the proposed ANFIS. This work shows a novel application of ANFIS with a hybrid learning algorithm for the AVR system. The ANFIS was designed by using the hybrid optimization learning scheme to train the Fuzzy Inference System (FIS) that is used to control the parameters of the AVR system. To generate the right dataset used for training the fuzzy inferential system, the Proportional-Integral-Derivative (PID) simulation of the entire system connected to the AVR is used setting the simulation time to about ten seconds. Simulation of the work was done in MATLAB/Simulink and enhanced performance objectives (rise time of 1.1994s, settling time of 1.8818, overshoot of 1.3206, and steady-state error of 4.269e-04) were compared to other related works. Automatic voltage regulator ANFIS PID Fuzzy Control Power System Science Q Suleiman Usman Hussein verfasserin aut Bemdoo Saka verfasserin aut Sadiq Umar Abubakar verfasserin aut Idoko S. Attah verfasserin aut In Scientific African Elsevier, 2018 19(2023), Seite e01573- (DE-627)1047200163 24682276 nnns volume:19 year:2023 pages:e01573- https://doi.org/10.1016/j.sciaf.2023.e01573 kostenfrei https://doaj.org/article/2d31f2b25a2b4f8f9e46d8027828eb4f kostenfrei http://www.sciencedirect.com/science/article/pii/S2468227623000327 kostenfrei https://doaj.org/toc/2468-2276 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2023 e01573-
allfieldsSound	10.1016/j.sciaf.2023.e01573 doi (DE-627)DOAJ080862454 (DE-599)DOAJ2d31f2b25a2b4f8f9e46d8027828eb4f DE-627 ger DE-627 rakwb eng Maman Jimoh Lawal verfasserin aut Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work focuses on enhancing the performance of an Automatic Voltage Regulator (AVR) by providing a good transient response, adaptability to changing circumstances, and robustness. Its objective is centered on utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) to control the AVR system. The study is important because it compares the performance of the system without a controller, with the PID controller and the proposed ANFIS. This work shows a novel application of ANFIS with a hybrid learning algorithm for the AVR system. The ANFIS was designed by using the hybrid optimization learning scheme to train the Fuzzy Inference System (FIS) that is used to control the parameters of the AVR system. To generate the right dataset used for training the fuzzy inferential system, the Proportional-Integral-Derivative (PID) simulation of the entire system connected to the AVR is used setting the simulation time to about ten seconds. Simulation of the work was done in MATLAB/Simulink and enhanced performance objectives (rise time of 1.1994s, settling time of 1.8818, overshoot of 1.3206, and steady-state error of 4.269e-04) were compared to other related works. Automatic voltage regulator ANFIS PID Fuzzy Control Power System Science Q Suleiman Usman Hussein verfasserin aut Bemdoo Saka verfasserin aut Sadiq Umar Abubakar verfasserin aut Idoko S. Attah verfasserin aut In Scientific African Elsevier, 2018 19(2023), Seite e01573- (DE-627)1047200163 24682276 nnns volume:19 year:2023 pages:e01573- https://doi.org/10.1016/j.sciaf.2023.e01573 kostenfrei https://doaj.org/article/2d31f2b25a2b4f8f9e46d8027828eb4f kostenfrei http://www.sciencedirect.com/science/article/pii/S2468227623000327 kostenfrei https://doaj.org/toc/2468-2276 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2086 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2023 e01573-
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author	Maman Jimoh Lawal
spellingShingle	Maman Jimoh Lawal misc Automatic voltage regulator misc ANFIS misc PID misc Fuzzy misc Control misc Power System misc Science misc Q Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method
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topic_title	Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method Automatic voltage regulator ANFIS PID Fuzzy Control Power System
topic	misc Automatic voltage regulator misc ANFIS misc PID misc Fuzzy misc Control misc Power System misc Science misc Q
topic_unstemmed	misc Automatic voltage regulator misc ANFIS misc PID misc Fuzzy misc Control misc Power System misc Science misc Q
topic_browse	misc Automatic voltage regulator misc ANFIS misc PID misc Fuzzy misc Control misc Power System misc Science misc Q
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title	Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method
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title_full	Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method
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author_browse	Maman Jimoh Lawal Suleiman Usman Hussein Bemdoo Saka Sadiq Umar Abubakar Idoko S. Attah
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title_sort	intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method
title_auth	Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method
abstract	This work focuses on enhancing the performance of an Automatic Voltage Regulator (AVR) by providing a good transient response, adaptability to changing circumstances, and robustness. Its objective is centered on utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) to control the AVR system. The study is important because it compares the performance of the system without a controller, with the PID controller and the proposed ANFIS. This work shows a novel application of ANFIS with a hybrid learning algorithm for the AVR system. The ANFIS was designed by using the hybrid optimization learning scheme to train the Fuzzy Inference System (FIS) that is used to control the parameters of the AVR system. To generate the right dataset used for training the fuzzy inferential system, the Proportional-Integral-Derivative (PID) simulation of the entire system connected to the AVR is used setting the simulation time to about ten seconds. Simulation of the work was done in MATLAB/Simulink and enhanced performance objectives (rise time of 1.1994s, settling time of 1.8818, overshoot of 1.3206, and steady-state error of 4.269e-04) were compared to other related works.
abstractGer	This work focuses on enhancing the performance of an Automatic Voltage Regulator (AVR) by providing a good transient response, adaptability to changing circumstances, and robustness. Its objective is centered on utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) to control the AVR system. The study is important because it compares the performance of the system without a controller, with the PID controller and the proposed ANFIS. This work shows a novel application of ANFIS with a hybrid learning algorithm for the AVR system. The ANFIS was designed by using the hybrid optimization learning scheme to train the Fuzzy Inference System (FIS) that is used to control the parameters of the AVR system. To generate the right dataset used for training the fuzzy inferential system, the Proportional-Integral-Derivative (PID) simulation of the entire system connected to the AVR is used setting the simulation time to about ten seconds. Simulation of the work was done in MATLAB/Simulink and enhanced performance objectives (rise time of 1.1994s, settling time of 1.8818, overshoot of 1.3206, and steady-state error of 4.269e-04) were compared to other related works.
abstract_unstemmed	This work focuses on enhancing the performance of an Automatic Voltage Regulator (AVR) by providing a good transient response, adaptability to changing circumstances, and robustness. Its objective is centered on utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) to control the AVR system. The study is important because it compares the performance of the system without a controller, with the PID controller and the proposed ANFIS. This work shows a novel application of ANFIS with a hybrid learning algorithm for the AVR system. The ANFIS was designed by using the hybrid optimization learning scheme to train the Fuzzy Inference System (FIS) that is used to control the parameters of the AVR system. To generate the right dataset used for training the fuzzy inferential system, the Proportional-Integral-Derivative (PID) simulation of the entire system connected to the AVR is used setting the simulation time to about ten seconds. Simulation of the work was done in MATLAB/Simulink and enhanced performance objectives (rise time of 1.1994s, settling time of 1.8818, overshoot of 1.3206, and steady-state error of 4.269e-04) were compared to other related works.
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title_short	Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method
url	https://doi.org/10.1016/j.sciaf.2023.e01573 https://doaj.org/article/2d31f2b25a2b4f8f9e46d8027828eb4f http://www.sciencedirect.com/science/article/pii/S2468227623000327 https://doaj.org/toc/2468-2276
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author2	Suleiman Usman Hussein Bemdoo Saka Sadiq Umar Abubakar Idoko S. Attah
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up_date	2024-07-03T16:56:47.150Z
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Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method

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