Iteratively reweighted least squares for maximum likelihood identification of synchronous machine parameters from on-line tests

Wamkeue, R.; Kamwa, Innocent; Dai-Do, X.; Keyhani, A.

doi:10.1109/60.766971

Cited by 37 publications

(15 citation statements)

References 12 publications

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“…2, the covariance matrix R(θ) is recalculated, leading to an iterative reweighting of the objective function (18). The identification process can be summarized in the three following steps [32].…”

Section: ML Parameter Identificationmentioning

confidence: 99%

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

Aguglia

Viarouge

Martins

2013

IEEE Trans. on Ind. Applicat.

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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. Index Terms-High-voltage (HV) techniques, identification

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Section: ML Parameter Identificationmentioning

confidence: 99%

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

Aguglia

Viarouge

Martins

2013

IEEE Trans. on Ind. Applicat.

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“…[defined in (8)]. Here, is the set of parameters that must be identified and is given by: (12) where is the set of admissible values of the parameter.…”

Section: Identification Of Parametersmentioning

confidence: 99%

“…The problem is reduced to fit an EC structure to the measured frequency response of the machine. The maximum-likelihood (ML) estimation approach is now a well accepted method for identification of SM parameters from frequency and time-domain tests [2]- [4], [8], where the minimum of a log likelihood function [8], [9] is sought. The minimization is generally performed using techniques based on Gauss-Newton, Neder-Mead or Levenberg-Maquard approaches [10].…”

Section: Introductionmentioning

confidence: 99%

Synchronous machine parameters from frequency-response finite-element simulations and genetic algorithms

Escarela‐Perez

Niewierowicz

Campero‐Littlewood

2001

IEEE Trans. On energy Conversion

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This paper presents a novel way to obtain parameters of synchronous-machine equivalent circuits from standstill frequency response data using a hybrid genetic algorithm. The genetic algorithm is capable of finding a global minimum within a search interval of the fitness function used to match the equivalent circuit and the measured machine transfer functions, notwithstanding the initial guess of the identification process. Therefore, methods such as the Maximum Likelihood Estimation technique, could be substantially enhanced. Results obtained in the identification procedure show that good matching can be obtained with either negative or positive leakage inductance values. These results cast some light on the possible physical meaning that circuit parameters may have. Finite-element modeling is used here to determine the transfer functions of a turbine generator. This approach is consistent with the general aim of obviating the requirement of field testing.

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“…To overcome the shortcomings of the traditional methods, and, to some extent, include unstructured nonlinearities, online identification methods have been suggested [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. These papers can be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

“…The second category [9][10][11][12][13][14][15][16][17][18][19][20] of papers assumes a known structure for the synchronous machine (as the traditional methods), and tries to estimate the physical parameters from on-line measurements. The main advantage of this category is that it yields the physical parameters.…”

Section: Introductionmentioning

confidence: 99%

Two New Methods for Synchronous Generator Parameter Estimation

Agahi

Karrari

Mahmoodzadeh

2007

2007 IEEE Lausanne Power Tech

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In this paper, two new methods for synchronous generator parameter estimation are presented. In the first method, a third order nonlinear model of the system is transformed to a linear regression form, without any linearization or simplification. Then, parameters are estimated using ordinary and parallel strategies. The second method indicates that if rotor speed, rotor angle and active and reactive powers are measured, all parameters can be estimated directly using simple approaches. The simulation results show good accuracy of the estimated parameters in both methods.

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Iteratively reweighted least squares for maximum likelihood identification of synchronous machine parameters from on-line tests

Cited by 37 publications

References 12 publications

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

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

Synchronous machine parameters from frequency-response finite-element simulations and genetic algorithms

Two New Methods for Synchronous Generator Parameter Estimation

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