Frequency-Domain Maximum-Likelihood Estimation of High-Voltage Pulse Transformer Model Parameters
(2013) In IEEE Transactions on Industry Applications 49(6). p.2552-2561- Abstract
- This paper presents an offline frequency-domain nonlinear and stochastic identification method for equivalent model parameter estimation of high-voltage pulse transformers. Such kinds of transformers are widely used in the pulsed-power domain, and the difficulty in deriving pulsed-power converter optimal control strategies is directly linked to the accuracy of the equivalent circuit parameters. These components require models which take into account electric fields energies represented by stray capacitance in the equivalent circuit. These capacitive elements must be accurately identified, since they greatly influence the general converter performances. A nonlinear frequency-based identification method, based on maximum-likelihood... (More)
- This paper presents an offline frequency-domain nonlinear and stochastic identification method for equivalent model parameter estimation of high-voltage pulse transformers. Such kinds of transformers are widely used in the pulsed-power domain, and the difficulty in deriving pulsed-power converter optimal control strategies is directly linked to the accuracy of the equivalent circuit parameters. These components require models which take into account electric fields energies represented by stray capacitance in the equivalent circuit. These capacitive elements must be accurately identified, since they greatly influence the general converter performances. A nonlinear frequency-based identification method, based on maximum-likelihood estimation, is presented, and a sensitivity analysis of the best experimental test to be considered is carried out. The procedure takes into account magnetic saturation and skin effects occurring in the windings during the frequency tests. The presented method is validated by experimental identification of a 2-MW-100-kV pulse transformer. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4273181
- author
- Aguglia, Davide ; Viarouge, Philippe and Martins, Carlos LU
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- High-voltage (HV) techniques, identification, maximum-likelihood (ML), optimization methods, pulse transformers
- in
- IEEE Transactions on Industry Applications
- volume
- 49
- issue
- 6
- pages
- 2552 - 2561
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- wos:000327552500023
- scopus:84890087639
- ISSN
- 0093-9994
- DOI
- 10.1109/TIA.2013.2265213
- language
- English
- LU publication?
- yes
- id
- db8cb446-60a2-471e-888b-74d80c22d73f (old id 4273181)
- date added to LUP
- 2016-04-01 14:45:48
- date last changed
- 2022-02-27 04:24:35
@article{db8cb446-60a2-471e-888b-74d80c22d73f,
abstract = {{This paper presents an offline frequency-domain nonlinear and stochastic identification method for equivalent model parameter estimation of high-voltage pulse transformers. Such kinds of transformers are widely used in the pulsed-power domain, and the difficulty in deriving pulsed-power converter optimal control strategies is directly linked to the accuracy of the equivalent circuit parameters. These components require models which take into account electric fields energies represented by stray capacitance in the equivalent circuit. These capacitive elements must be accurately identified, since they greatly influence the general converter performances. A nonlinear frequency-based identification method, based on maximum-likelihood estimation, is presented, and a sensitivity analysis of the best experimental test to be considered is carried out. The procedure takes into account magnetic saturation and skin effects occurring in the windings during the frequency tests. The presented method is validated by experimental identification of a 2-MW-100-kV pulse transformer.}},
author = {{Aguglia, Davide and Viarouge, Philippe and Martins, Carlos}},
issn = {{0093-9994}},
keywords = {{High-voltage (HV) techniques; identification; maximum-likelihood (ML); optimization methods; pulse transformers}},
language = {{eng}},
number = {{6}},
pages = {{2552--2561}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Industry Applications}},
title = {{Frequency-Domain Maximum-Likelihood Estimation of High-Voltage Pulse Transformer Model Parameters}},
url = {{http://dx.doi.org/10.1109/TIA.2013.2265213}},
doi = {{10.1109/TIA.2013.2265213}},
volume = {{49}},
year = {{2013}},
}