Application of Deep Learning in Parameter Estimation of Permanent Magnet Synchronous Machines
This paper presents a novel method for real-time identification of four parameters of the permanent magnet synchronous machines (PMSM) namely stator resistance, d-axis inductance, q-axis inductance and the rotor flux linkage. The proposed method is based on the utilization of the deep neural network...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10472511/ |
| _version_ | 1797243415698604032 |
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| author | Minh Xuan Bui Rukmi Dutta Faz Rahman |
| author_facet | Minh Xuan Bui Rukmi Dutta Faz Rahman |
| author_sort | Minh Xuan Bui |
| collection | DOAJ |
| description | This paper presents a novel method for real-time identification of four parameters of the permanent magnet synchronous machines (PMSM) namely stator resistance, d-axis inductance, q-axis inductance and the rotor flux linkage. The proposed method is based on the utilization of the deep neural network to solve the problems of the existing model-based parameter estimation methods, which are caused by the non-linearity of the inverter and the inaccuracy of the measured rotor position. Extensive numerical simulations and experimental studies have been conducted to evaluate the robustness and the accuracy of the proposed online parameters identification solution, compared with the conventional methods such as recursive least square, extended Kalman filter and Adaline neural network. |
| first_indexed | 2024-04-24T18:54:45Z |
| format | Article |
| id | doaj.art-1391db53489a4b47b357f43e7187c48c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:45Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1391db53489a4b47b357f43e7187c48c2024-03-26T17:44:08ZengIEEEIEEE Access2169-35362024-01-0112407104072110.1109/ACCESS.2024.337722410472511Application of Deep Learning in Parameter Estimation of Permanent Magnet Synchronous MachinesMinh Xuan Bui0https://orcid.org/0000-0001-8591-7413Rukmi Dutta1https://orcid.org/0000-0002-8232-3537Faz Rahman2https://orcid.org/0000-0001-9443-320XFaculty of Control Engineering, Le Quy Don Technical University, Hanoi, VietnamSchool of Electrical and Telecommunications, University of New South Wales, Sydney, NSW, AustraliaSchool of Electrical and Telecommunications, University of New South Wales, Sydney, NSW, AustraliaThis paper presents a novel method for real-time identification of four parameters of the permanent magnet synchronous machines (PMSM) namely stator resistance, d-axis inductance, q-axis inductance and the rotor flux linkage. The proposed method is based on the utilization of the deep neural network to solve the problems of the existing model-based parameter estimation methods, which are caused by the non-linearity of the inverter and the inaccuracy of the measured rotor position. Extensive numerical simulations and experimental studies have been conducted to evaluate the robustness and the accuracy of the proposed online parameters identification solution, compared with the conventional methods such as recursive least square, extended Kalman filter and Adaline neural network.https://ieeexplore.ieee.org/document/10472511/Online parameter identificationPMSMdeep learningneural networkrecursive least squareextend Kalman filter |
| spellingShingle | Minh Xuan Bui Rukmi Dutta Faz Rahman Application of Deep Learning in Parameter Estimation of Permanent Magnet Synchronous Machines IEEE Access Online parameter identification PMSM deep learning neural network recursive least square extend Kalman filter |
| title | Application of Deep Learning in Parameter Estimation of Permanent Magnet Synchronous Machines |
| title_full | Application of Deep Learning in Parameter Estimation of Permanent Magnet Synchronous Machines |
| title_fullStr | Application of Deep Learning in Parameter Estimation of Permanent Magnet Synchronous Machines |
| title_full_unstemmed | Application of Deep Learning in Parameter Estimation of Permanent Magnet Synchronous Machines |
| title_short | Application of Deep Learning in Parameter Estimation of Permanent Magnet Synchronous Machines |
| title_sort | application of deep learning in parameter estimation of permanent magnet synchronous machines |
| topic | Online parameter identification PMSM deep learning neural network recursive least square extend Kalman filter |
| url | https://ieeexplore.ieee.org/document/10472511/ |
| work_keys_str_mv | AT minhxuanbui applicationofdeeplearninginparameterestimationofpermanentmagnetsynchronousmachines AT rukmidutta applicationofdeeplearninginparameterestimationofpermanentmagnetsynchronousmachines AT fazrahman applicationofdeeplearninginparameterestimationofpermanentmagnetsynchronousmachines |