Regularisation approach for real-time modelling of aero gas turbines

Breikin, Tim; Arkov, V.Y.; Куликов, Г.Г.

doi:10.1016/s0967-0661(03)00107-2

Cited by 21 publications

(13 citation statements)

References 8 publications

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“…The motivation of this paper is the identification of uncertainty in a nonlinear actuator characteristic in closed-loop operation. Breikin et al (2004) demonstrated that low-order linear plant models effectively capture the relationship from fuel flow to output power and Dai and Wang (2006) presented similar results for the relationship from fuel flow to shaft speed. Measurement and simulation data are only available from closedloop operation.…”

Section: Introductionmentioning

confidence: 91%

“…Define n * a and n * b as such that the linear plant model G(q,η) capture the plant dynamics. Breikin et al (2004) offers insight that first order linear models perform adequately around local operating points. For initialization, we increase the orders of n a and n b to minimize the bias from the nonlinear distortions and unmodeled dynamics on the dynamic plant in the initialization.…”

Section: Initializationmentioning

confidence: 99%

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Closed-Loop Identification of Hammerstein Systems with Application to Gas Turbines

Holcomb

Callafon

Bitmead

2014

IFAC Proceedings Volumes

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Many practical applications, such as the fuel control of a gas turbine engine, can be modeled by a feedback connection of a linear controller in series with a Hammerstein system, where the nonlinearity provides a representation of the control element or actuator. An iterative gradient-based method is proposed to simultaneously identify the nonlinear fuel valve characteristic and a low-order linear plant model in gas turbine applications that leverages a priori knowledge of both the nonlinearity and engine dynamics. The identification is a nonlinear prediction error minimization method in a closedloop Hammerstein model framework. It is applied to data from a high-fidelity simulation of a 5 megawatt Taurus T M 60 industrial gas turbine.

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Section: Introductionmentioning

confidence: 91%

Section: Initializationmentioning

confidence: 99%

Closed-Loop Identification of Hammerstein Systems with Application to Gas Turbines

Holcomb

Callafon

Bitmead

2014

IFAC Proceedings Volumes

View full text Add to dashboard Cite

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“…This tool has been long known and used in statistical parameter estimation but was not common in system identification until recently [28]. T. Breikin et al presented a regularization-based approach to the estimation of engine model coefficients [29]. It was based on the regularity of the frequency response pattern over the operation range of the engine, but that method is not universal.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

et al. 2020

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Zero-dimensional models based on the description of the thermo-gas-dynamic process are widely used in the design of engines and their control and diagnostic systems. The models are subjected to an identification procedure to bring their outputs as close as possible to experimental data and assess engine health. This paper aims to improve the stability of engine model identification when the number of measured parameters is small, and their measurement error is not negligible. The proposed method for the estimation of engine components’ parameters, based on multi-criteria identification, provides stable estimations and their confidence intervals within known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularized identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Synthesis of objective functions and subsequent scalar convolutions of these functions are used to estimate gas-path components’ parameters. A comparison with traditional methods showed that the main advantage of the proposed approach is the high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions that do not correspond to a priori assumptions and also helps to implement the gas path analysis with a limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, gas path analysis and adapting dynamic models to the needs of the engine control system.

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“…Breikin et al presented regularisation-based approach to the model coefficients estimation which was based on the regularity of the frequency response pattern over the operation range of the engine [27] but that method is not universal. The Marquardt's method of regularization implemented here was also used by Guseynov et al [28,29] but they did not analyse the bias nor consider how the regularization coefficient influences the bias and the noise which could impede practical applications.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

Khustochka¹,

Yepifanov²,

Zelenskyi³

et al. 2019

Preprint

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Gas Path Analysis and matching turbine engine models to experimental data are inverse problems of mathematical modelling which are characterized by parametric uncertainty. This results from the fact that the number of measured parameters is significantly lower than the number of components’ performance parameters needed to describe the real engine. In these conditions, even small measurement errors can result in a high variation of results, and obtained efficiency, loss factors etc. can appear out of the physical range. The current methods of engine model identification have developed considerably to provide stable, precise and physically adequate solutions. Presented in this work is an estimation method of engine components’ parameters based on multi-criteria identification which provides stable estimations of parameters and their confidence intervals with the known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularised identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Forming objective functions and scalar convolutions synthesis of these functions is used to estimate gas-path components’ parameters. A comparison of the proposed approach with traditional methods showed that its main advantage is high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions which do not correspond to a priori assumptions, and also helps to implement the Gas Path Analysis at the limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, Gas Path Analysis and also adapting dynamic models for the needs of the engine control system.

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Regularisation approach for real-time modelling of aero gas turbines

Cited by 21 publications

References 8 publications

Closed-Loop Identification of Hammerstein Systems with Application to Gas Turbines

Closed-Loop Identification of Hammerstein Systems with Application to Gas Turbines

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

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