Estimation of Induction Motor Parameters Using Hybrid Algorithms for Power System Dynamic Studies
This paper proposes a hybrid Newton-Raphson and genetic algorithm for the estimation of double cage induction motor parameters from commonly available manufacturer data. The hybrid algorithm was tested on a large data set of 6,380 IEC and NEMA motors and then compared with a baseline Newton-Raphson...
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| Other Authors: | |
| Format: | Conference Paper |
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University of Tasmania
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/23117 |
| _version_ | 1848751060853719040 |
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| author | Susanto, Julius Islam, Syed |
| author2 | Michael Negnevitsky |
| author_facet | Michael Negnevitsky Susanto, Julius Islam, Syed |
| author_sort | Susanto, Julius |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes a hybrid Newton-Raphson and genetic algorithm for the estimation of double cage induction motor parameters from commonly available manufacturer data. The hybrid algorithm was tested on a large data set of 6,380 IEC and NEMA motors and then compared with a baseline Newton-Raphson algorithm. The simulation results show that while the proposed hybrid algorithm is more computationally intensive, it does make significant improvements to convergence and error rates. |
| first_indexed | 2025-11-14T07:46:44Z |
| format | Conference Paper |
| id | curtin-20.500.11937-23117 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:46:44Z |
| publishDate | 2013 |
| publisher | University of Tasmania |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-231172017-09-13T13:59:01Z Estimation of Induction Motor Parameters Using Hybrid Algorithms for Power System Dynamic Studies Susanto, Julius Islam, Syed Michael Negnevitsky hybrid algorithm parameter estimation Induction motor This paper proposes a hybrid Newton-Raphson and genetic algorithm for the estimation of double cage induction motor parameters from commonly available manufacturer data. The hybrid algorithm was tested on a large data set of 6,380 IEC and NEMA motors and then compared with a baseline Newton-Raphson algorithm. The simulation results show that while the proposed hybrid algorithm is more computationally intensive, it does make significant improvements to convergence and error rates. 2013 Conference Paper http://hdl.handle.net/20.500.11937/23117 10.1109/AUPEC.2013.6725462 University of Tasmania restricted |
| spellingShingle | hybrid algorithm parameter estimation Induction motor Susanto, Julius Islam, Syed Estimation of Induction Motor Parameters Using Hybrid Algorithms for Power System Dynamic Studies |
| title | Estimation of Induction Motor Parameters Using Hybrid Algorithms for Power System Dynamic Studies |
| title_full | Estimation of Induction Motor Parameters Using Hybrid Algorithms for Power System Dynamic Studies |
| title_fullStr | Estimation of Induction Motor Parameters Using Hybrid Algorithms for Power System Dynamic Studies |
| title_full_unstemmed | Estimation of Induction Motor Parameters Using Hybrid Algorithms for Power System Dynamic Studies |
| title_short | Estimation of Induction Motor Parameters Using Hybrid Algorithms for Power System Dynamic Studies |
| title_sort | estimation of induction motor parameters using hybrid algorithms for power system dynamic studies |
| topic | hybrid algorithm parameter estimation Induction motor |
| url | http://hdl.handle.net/20.500.11937/23117 |