Online d-q axis inductance identification for IPMSMs using FEA-driven CNN
The permanent magnet synchronous motor (PMSM) is the most commonly used option for electric vehicles, because it has a straightforward design and a comparatively high power-density. For the sake of healthy monitoring and sophisticated parameter-dependent control theories for PMSMs, determining the p...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447924005112 |
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| author | Ruofeng Yao |
| author_facet | Ruofeng Yao |
| author_sort | Ruofeng Yao |
| collection | DOAJ |
| description | The permanent magnet synchronous motor (PMSM) is the most commonly used option for electric vehicles, because it has a straightforward design and a comparatively high power-density. For the sake of healthy monitoring and sophisticated parameter-dependent control theories for PMSMs, determining the parameters of PMSMs is crucial. Precise identification of the inductance is required due to its coupled and nonlinear connection with other electromagnetic properties. In this paper, a convolutional neural network (CNN) model is designed to identify the d-q axis inductances of an interior permanent magnet synchronous motor (IPMSM). The model is trained with datasets obtained by finite element analysis (FEA) methods. Simulation validates that the proposed model performs excellently in terms of online identification, yielding maximum bias values of 2.96 % for the q-axis inductance and 2.11% for the d-axis inductance. The proposed method achieves accurate inductance online identification providing a new solution to handle nonlinear industrial problems. |
| format | Article |
| id | doaj-art-0bc3163dc2334b339d3ac3edb773e637 |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-0bc3163dc2334b339d3ac3edb773e6372024-12-18T08:48:28ZengElsevierAin Shams Engineering Journal2090-44792024-12-011512103130Online d-q axis inductance identification for IPMSMs using FEA-driven CNNRuofeng Yao0School of Mechanical Engineering, City University of Hong Kong ,999077, Hong Kong, ChinaThe permanent magnet synchronous motor (PMSM) is the most commonly used option for electric vehicles, because it has a straightforward design and a comparatively high power-density. For the sake of healthy monitoring and sophisticated parameter-dependent control theories for PMSMs, determining the parameters of PMSMs is crucial. Precise identification of the inductance is required due to its coupled and nonlinear connection with other electromagnetic properties. In this paper, a convolutional neural network (CNN) model is designed to identify the d-q axis inductances of an interior permanent magnet synchronous motor (IPMSM). The model is trained with datasets obtained by finite element analysis (FEA) methods. Simulation validates that the proposed model performs excellently in terms of online identification, yielding maximum bias values of 2.96 % for the q-axis inductance and 2.11% for the d-axis inductance. The proposed method achieves accurate inductance online identification providing a new solution to handle nonlinear industrial problems.http://www.sciencedirect.com/science/article/pii/S2090447924005112IPMSMCNNFinite element analysisInductanceOnline identification |
| spellingShingle | Ruofeng Yao Online d-q axis inductance identification for IPMSMs using FEA-driven CNN Ain Shams Engineering Journal IPMSM CNN Finite element analysis Inductance Online identification |
| title | Online d-q axis inductance identification for IPMSMs using FEA-driven CNN |
| title_full | Online d-q axis inductance identification for IPMSMs using FEA-driven CNN |
| title_fullStr | Online d-q axis inductance identification for IPMSMs using FEA-driven CNN |
| title_full_unstemmed | Online d-q axis inductance identification for IPMSMs using FEA-driven CNN |
| title_short | Online d-q axis inductance identification for IPMSMs using FEA-driven CNN |
| title_sort | online d q axis inductance identification for ipmsms using fea driven cnn |
| topic | IPMSM CNN Finite element analysis Inductance Online identification |
| url | http://www.sciencedirect.com/science/article/pii/S2090447924005112 |
| work_keys_str_mv | AT ruofengyao onlinedqaxisinductanceidentificationforipmsmsusingfeadrivencnn |