Optimal Neuro-fuzzy model and PID controller of a Hydro-generator unit from the identification of its LTI model
This paper describes a methodology to obtain a neuro-fuzzy model of a hydro-generator unit (HGU) and its PID controller from the identification of its linear time-invariant (LTI) model. The study performs a cause-effect record, which allows a continuous time identification of the LTI type, then obta...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823002016 |
| _version_ | 1797857660460072960 |
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| author | Jesús Rodríguez-Flores Victor Herrera-Perez Mayra Pacheco-Cunduri Jorge Hernández-Ambato Alejandro Paredes-Camacho Miguel Delgado-Prieto |
| author_facet | Jesús Rodríguez-Flores Victor Herrera-Perez Mayra Pacheco-Cunduri Jorge Hernández-Ambato Alejandro Paredes-Camacho Miguel Delgado-Prieto |
| author_sort | Jesús Rodríguez-Flores |
| collection | DOAJ |
| description | This paper describes a methodology to obtain a neuro-fuzzy model of a hydro-generator unit (HGU) and its PID controller from the identification of its linear time-invariant (LTI) model. The study performs a cause-effect record, which allows a continuous time identification of the LTI type, then obtaining a classic PID controller capable of complying with a performance specification given by a pole of a second order system, which enables the training of the proposed neuro-fuzzy model. Applying the cost function of the root mean square error and the root of the percentage relative mean square error, the system parameters were adjusted using the decreasing gradient method. By means of linear models, the initialization of the singletons of the neuro-fuzzy models was done in two stages using the decreasing gradient and a cost function. The first stage was carried out without dynamics and the second stage with the dynamics of the simulated system. A case study of the Hydro-Agoyán HGU was selected and the results showed that the development of the LTI model allowed the development of the neuro-fuzzy model able to represent the behavior of the power plant and its response under variations of the power setpoint of 5, 10, 15 and 20%. |
| first_indexed | 2024-04-09T21:00:21Z |
| format | Article |
| id | doaj.art-07360dddd37048d2ad63ddbf40417a72 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-09T21:00:21Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-07360dddd37048d2ad63ddbf40417a722023-03-29T09:25:14ZengElsevierAlexandria Engineering Journal1110-01682023-05-0171309337Optimal Neuro-fuzzy model and PID controller of a Hydro-generator unit from the identification of its LTI modelJesús Rodríguez-Flores0Victor Herrera-Perez1Mayra Pacheco-Cunduri2Jorge Hernández-Ambato3Alejandro Paredes-Camacho4Miguel Delgado-Prieto5Universidad Regional Autónoma de Los Andes (UNIANDES), Ambato 180101, EcuadorUniversidad San Francisco de Quito – USFQ, Institute for Energy and Materials, Quito, 170901, Ecuador; Corresponding author.Escuela Superior Politécnica de Chimborazo (ESPOCH), Facultad de Informática y Electrónica, Riobamba, 060101, EcuadorEscuela Superior Politécnica de Chimborazo (ESPOCH), Facultad de Informática y Electrónica, Riobamba, 060101, EcuadorUniversitat Politècnica de Catalunya (UPC), Electronic Engineering Department, MCIA Research Group, Terrassa, 08222, SpainUniversitat Politècnica de Catalunya (UPC), Electronic Engineering Department, MCIA Research Group, Terrassa, 08222, SpainThis paper describes a methodology to obtain a neuro-fuzzy model of a hydro-generator unit (HGU) and its PID controller from the identification of its linear time-invariant (LTI) model. The study performs a cause-effect record, which allows a continuous time identification of the LTI type, then obtaining a classic PID controller capable of complying with a performance specification given by a pole of a second order system, which enables the training of the proposed neuro-fuzzy model. Applying the cost function of the root mean square error and the root of the percentage relative mean square error, the system parameters were adjusted using the decreasing gradient method. By means of linear models, the initialization of the singletons of the neuro-fuzzy models was done in two stages using the decreasing gradient and a cost function. The first stage was carried out without dynamics and the second stage with the dynamics of the simulated system. A case study of the Hydro-Agoyán HGU was selected and the results showed that the development of the LTI model allowed the development of the neuro-fuzzy model able to represent the behavior of the power plant and its response under variations of the power setpoint of 5, 10, 15 and 20%.http://www.sciencedirect.com/science/article/pii/S1110016823002016LTI modelNeuro-fuzzy controlPID controlPI + D controlHydro-generator unit |
| spellingShingle | Jesús Rodríguez-Flores Victor Herrera-Perez Mayra Pacheco-Cunduri Jorge Hernández-Ambato Alejandro Paredes-Camacho Miguel Delgado-Prieto Optimal Neuro-fuzzy model and PID controller of a Hydro-generator unit from the identification of its LTI model Alexandria Engineering Journal LTI model Neuro-fuzzy control PID control PI + D control Hydro-generator unit |
| title | Optimal Neuro-fuzzy model and PID controller of a Hydro-generator unit from the identification of its LTI model |
| title_full | Optimal Neuro-fuzzy model and PID controller of a Hydro-generator unit from the identification of its LTI model |
| title_fullStr | Optimal Neuro-fuzzy model and PID controller of a Hydro-generator unit from the identification of its LTI model |
| title_full_unstemmed | Optimal Neuro-fuzzy model and PID controller of a Hydro-generator unit from the identification of its LTI model |
| title_short | Optimal Neuro-fuzzy model and PID controller of a Hydro-generator unit from the identification of its LTI model |
| title_sort | optimal neuro fuzzy model and pid controller of a hydro generator unit from the identification of its lti model |
| topic | LTI model Neuro-fuzzy control PID control PI + D control Hydro-generator unit |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823002016 |
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