F. Kent, W.
(2003)
Machine learning for parameter identification of electric induction machines.
PhD thesis, University of Liverpool.
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Abstract
This thesis is concerned with the application of simulated evolution (SE) to the steady-state parameter identification problem of a simulated and real 3-phase induction machine, over the no-load direct-on-line start period. In the case of the simulated 3-phase induction machine, the Kron's two-axis dynamic mathematical model was used to generate the real and simulated system responses where the induction machine parameters remain constant over the entire range of slip. The model was used in the actual value as well as the per-unit system, and the parameters were estimated using both the genetic algorithm (GA) and the evolutionary programming (EP) from the machine's dynamic response to a direct-on-line start. Two measurement vectors represented the dynamic responses and all the parameter identification processes were subject to five different levels of measurement noise. For the case of the real 3-phase induction machine, the real system responses were generated by the real 3-phase induction machine whilst the simulated system responses were generated by the Kron's model. However, the real induction machine's parameters are not constant over the range of slip, because of the nonlinearities caused by the skin effect and saturation. Therefore, the parameter identification of a real3-phase induction machine, using EP from the machine's dynamic response to a direct-on-line start, was not possible by applying the same methodology used for estimating the parameters of the simulated, constant parameters, 3-phase induction machine.
| Item Type: | Thesis (PhD) |
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| Depositing User: | Symplectic Admin |
| Date Deposited: | 20 Oct 2023 15:44 |
| Last Modified: | 20 Oct 2023 15:45 |
| DOI: | 10.17638/03175173 |
| Copyright Statement: | Copyright © and Moral Rights for this thesis and any accompanying data (where applicable) are retained by the author and/or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3175173 |
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