Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive
This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/18/4946 |
| _version_ | 1797553091839524864 |
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| author | Tuan Pham Van Dung Vo Tien Zbigniew Leonowicz Michal Jasinski Tomasz Sikorski Prasun Chakrabarti |
| author_facet | Tuan Pham Van Dung Vo Tien Zbigniew Leonowicz Michal Jasinski Tomasz Sikorski Prasun Chakrabarti |
| author_sort | Tuan Pham Van |
| collection | DOAJ |
| description | This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small. |
| first_indexed | 2024-03-10T16:10:26Z |
| format | Article |
| id | doaj.art-3ebe1b9d04b2464ea8904bed98a38f3e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T16:10:26Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3ebe1b9d04b2464ea8904bed98a38f3e2023-11-20T14:29:34ZengMDPI AGEnergies1996-10732020-09-011318494610.3390/en13184946Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor DriveTuan Pham Van0Dung Vo Tien1Zbigniew Leonowicz2Michal Jasinski3Tomasz Sikorski4Prasun Chakrabarti5Faculty of Electrical Engineering, Vinh University of Technology Education, 117 Nguyen Viet Xuan Street, Vinh City 890000, VietnamFaculty of Electrical Engineering, Vinh University of Technology Education, 117 Nguyen Viet Xuan Street, Vinh City 890000, VietnamFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandFaculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandDepartment of Computer Science and Engineering, Techno India NJR Institute of Technology Udaipur, Rajasthan 313003, IndiaThis paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small.https://www.mdpi.com/1996-1073/13/18/4946rotor resistance estimationstator resistance estimationsensorless controlartificial neural network (ANN)indirect field-oriented control (IFOC) |
| spellingShingle | Tuan Pham Van Dung Vo Tien Zbigniew Leonowicz Michal Jasinski Tomasz Sikorski Prasun Chakrabarti Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive Energies rotor resistance estimation stator resistance estimation sensorless control artificial neural network (ANN) indirect field-oriented control (IFOC) |
| title | Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive |
| title_full | Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive |
| title_fullStr | Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive |
| title_full_unstemmed | Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive |
| title_short | Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive |
| title_sort | online rotor and stator resistance estimation based on artificial neural network applied in sensorless induction motor drive |
| topic | rotor resistance estimation stator resistance estimation sensorless control artificial neural network (ANN) indirect field-oriented control (IFOC) |
| url | https://www.mdpi.com/1996-1073/13/18/4946 |
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