Frequency-Domain Maximum-Likelihood Estimation of High-Voltage Pulse Transformer Model Parameters

Davide Aguglia, Philippe Viarouge, Carlos Martins

    Research output: Contribution to journalArticlepeer-review

    6 Citations (SciVal)

    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.
    Original languageEnglish
    Pages (from-to)2552-2561
    JournalIEEE Transactions on Industry Applications
    Volume49
    Issue number6
    DOIs
    Publication statusPublished - 2013

    Subject classification (UKÄ)

    • Natural Sciences
    • Physical Sciences

    Keywords

    • High-voltage (HV) techniques
    • identification
    • maximum-likelihood (ML)
    • optimization methods
    • pulse transformers

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