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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Scientific African |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468227623000327 |
| _version_ | 1811159600487989248 |
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| author | Maman Jimoh Lawal Suleiman Usman Hussein Bemdoo Saka Sadiq Umar Abubakar Idoko S. Attah |
| author_facet | Maman Jimoh Lawal Suleiman Usman Hussein Bemdoo Saka Sadiq Umar Abubakar Idoko S. Attah |
| author_sort | Maman Jimoh Lawal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T05:43:59Z |
| format | Article |
| id | doaj.art-2d31f2b25a2b4f8f9e46d8027828eb4f |
| institution | Directory Open Access Journal |
| issn | 2468-2276 |
| language | English |
| last_indexed | 2024-04-10T05:43:59Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj.art-2d31f2b25a2b4f8f9e46d8027828eb4f2023-03-06T04:18:29ZengElsevierScientific African2468-22762023-03-0119e01573Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning methodMaman Jimoh Lawal0Suleiman Usman Hussein1Bemdoo Saka2Sadiq Umar Abubakar3Idoko S. Attah4Electrical and Electronics Engineering Department, Nile University of Nigeria, Abuja, NigeriaElectrical and Electronics Engineering Department, Nile University of Nigeria, Abuja, Nigeria; Centre for Satellite Technology Development, National Space Research, and Development Agency, Abuja, NigeriaElectrical and Electronics Engineering Department, Nile University of Nigeria, Abuja, Nigeria; Corresponding author.Electrical and Electronics Engineering Department, Nile University of Nigeria, Abuja, Nigeria; Centre for Satellite Technology Development, National Space Research, and Development Agency, Abuja, NigeriaEngineering and Space Systems, National Space Research and Development Agency, Abuja, NigeriaThis 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.http://www.sciencedirect.com/science/article/pii/S2468227623000327Automatic voltage regulatorANFISPIDFuzzyControlPower System |
| spellingShingle | Maman Jimoh Lawal Suleiman Usman Hussein Bemdoo Saka Sadiq Umar Abubakar Idoko S. Attah Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method Scientific African Automatic voltage regulator ANFIS PID Fuzzy Control Power System |
| title | Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method |
| title_full | Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method |
| title_fullStr | Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method |
| title_full_unstemmed | Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method |
| title_short | Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method |
| title_sort | intelligent fuzzy based automatic voltage regulator with hybrid optimization learning method |
| topic | Automatic voltage regulator ANFIS PID Fuzzy Control Power System |
| url | http://www.sciencedirect.com/science/article/pii/S2468227623000327 |
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