On the Local Identifiability of Load Model Parameters in Measurement-based Approach

Choi, Byoung-Kon; Chiang, Hsiao‐Dong

doi:10.5370/jeet.2009.4.2.149

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…Identifiability analysis can be carried out either structurally or experimentally. A structural non-identifiability arises when there are redundant parameters in the model structure [26]. In experimental identifiability analysis, identifiability is tested by finding out if the measured information is enough to estimate the parameters reliably or not.…”

Section: Alternating Conditional Expectation (Ace)mentioning

confidence: 99%

On the Identifiability of Steady-State Induction Machine Models Using External Measurements

Alturas

Gadoue

Zahawi

et al. 2016

IEEE Trans. Energy Convers.

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Newcastle University ePrints -eprint.ncl.ac.uk Alturas AM, Gadoue SM, Zahawi B, Elgendy MA. On the Identifiability of Steady State Induction Machine Models using External Measurements. IEEE Transactions on Energy Conversion 2015. Abstract-A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the Alternating Conditional Expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1 kW three-phase induction machine in conjunction with the Levenberg-Marquardt (L-M) algorithm which is developed and applied here for this purpose. Index Terms: induction motor (IM), identifiability analysis, parameter identification.A. M. Alturas is with the School

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Section: Alternating Conditional Expectation (Ace)mentioning

confidence: 99%

On the Identifiability of Steady-State Induction Machine Models Using External Measurements

Alturas

Gadoue

Zahawi

et al. 2016

IEEE Trans. Energy Convers.

View full text Add to dashboard Cite

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“…The problem of parameters' correlation and identifiability is well known in the literature addressing regression analysis (cf. Brun et al, 2001;Choi and Chiang, 2009;Gelfand et al, 1995).…”

Section: Model Outputsmentioning

confidence: 99%

A novel population balance model to investigate the kinetics of in vitro cell proliferation: Part II. numerical solution, parameters' determination, and model outcomes

Fadda

Cincotti

Cao

2011

Biotech & Bioengineering

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Based on the general theoretical model developed in Part I of this work, a series of numerical simulations related to the in vitro proliferation kinetics of adherent cells is here presented. First the complex task of assigning a specific value to all the parameters of the proposed population balance (PB) model is addressed, by also highlighting the difficulties arising when performing proper comparisons with experimental data. Then, a parametric sensitivity analysis is performed, thus identifying the more relevant parameters from a kinetics perspective. The proposed PB model can be adapted to describe cell growth under various conditions, by properly changing the value of the adjustable parameters. For this reason, model parameters able to mimic cell culture behavior under microgravity conditions are identified by means of a suitable parametric sensitivity analysis. Specifically, it is found that, as the volume growth parameter is reduced, proliferation slows down while cells arrest in G0/G1 or G2/M depending on the initial distribution of cell population. On the basis of this result, model capabilities have been tested by means of a proper comparison with literature experimental data related to the behavior of synchronized and not-synchronized cells under micro- and standard gravity levels.

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New Power Quality Index in a Distribution Power System by Using RMP Model

Lee

Park

Venayagamoorthy

2008

2008 IEEE Industry Applications Society Annual Meeting

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In this paper, a new power quality index (PQI), which is directly related to the generation of distortion power from nonlinear harmonic loads, is introduced to determine their harmonic pollution ranking in a distribution power system. The electric load composition rate (LCR) and the total harmonic distortion (THD) for the estimated currents on each harmonic load are used to define the proposed PQI. The reduced multivariate polynomial (RMP) model with one-shot training property is applied to realize the PQI. Then, the ranking of distortion power for each nonlinear load, which have adverse effect on the entire system, is determined. It is proved that the relative ranking based on the PQI matches that on the distortion power computed directly from each harmonic load.

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On the Local Identifiability of Load Model Parameters in Measurement-based Approach

Cited by 8 publications

References 22 publications

On the Identifiability of Steady-State Induction Machine Models Using External Measurements

On the Identifiability of Steady-State Induction Machine Models Using External Measurements

A novel population balance model to investigate the kinetics of in vitro cell proliferation: Part II. numerical solution, parameters' determination, and model outcomes

New Power Quality Index in a Distribution Power System by Using RMP Model

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