The Parameter Estimation for a Non-linear Mathematical Model of Boiler-turbine-generator Unit

2007 ◽  
Author(s):  
Han Zhong-xu ◽  
Gao Chun-yu ◽  
Li Dan
Keyword(s):  
Mathematical Model ◽  
Parameter Estimation ◽  
Turbine Generator ◽  
Non Linear ◽  
Generator Unit

scholarly journals Optimal parameter estimation for a DC motor using genetic algorithm

2020 ◽  
Vol 11 (2) ◽  
pp. 1047 ◽  
Author(s):  
Mohammad Soleimani Amiri ◽  
Mohd Faisal Ibrahim ◽  
Rizauddin Ramli
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Parameter Estimation ◽  
Numerical Analysis ◽  
Optimal Parameter ◽  
Dc Motor ◽  
Linear Features ◽  
Estimated Parameters ◽  
Optimal Parameter Estimation ◽  
Non Linear

Estimating the parameters of a geared DC motor is crucial in terms of its non-linear features. In this paper, parameters of a geared DC motor are estimated genetically. Mathematical model of the DC motor is determined by Kirchhoff’s law and dynamic model of its shafts and gearbox. Parameters of the geared DC motor are initially estimated by MATLAB/SIMULINK. The estimated parameters are defined as initial values for Genetic Algorithm (GA) to minimize the error of the simulated and actual angular trajectory captured by an encoder. The optimal estimated model of the geared DC motor is validated by different voltages as the input of the actual DC motor and its mathematical model. The results and numerical analysis illustrate it can be ascertained that GA is appropriate to estimate the parameters of platforms with non linear characteristics.

scholarly journals Stability and Sensitivity Analysis and Optimization Control of the Hydro-turbine Generator Unit

2021 ◽  
Author(s):  
Yousong Shi ◽  
Jianzhong Zhou
Keyword(s):  
Mathematical Model ◽  
Optimal Control ◽  
Stable Operation ◽  
Control Parameters ◽  
Optimization Strategy ◽  
Turbine Generator ◽  
Hydro Turbine ◽  
Multi Objective ◽  
Nonlinear Dynamic Characteristics ◽  
Generator Unit

Abstract The hydro-turbine governing system (HTGS) and shafting system are mutually coupled. However, the interaction between them has always been neglected. This paper aims to explore the stability and sensitivity of the governor control parameters to the HTGS and shafting system and make the optimal control of the stable operation for the hydro-turbine generator unit(HTGU). First, a novel HTGU motion equation is proposed, which can make connections between the HTGS and the shafting system of the HTGU. And on this basis, the nonlinear coupling mathematical model of the HTGS and the shafting system is established. According to the nonlinear mathematical model, the sensitivity of the governor control parameters on the operating stability of the HTGU is obtained. Then, a multi-objective governor control parameters optimization strategy is proposed. Furthermore, the chaotic-dominated sorting genetic algorithm II(NSGA-II) and multi-objective evolutionary algorithm based on decomposition(MOEAD) were introduced to obtain the optimal control parameter and mutually verify the effectiveness of the optimization effect. Finally, the nonlinear dynamic characteristics of HTGU under optimal control were revealed. The simulation results show that the rotation speed deviation and shafting system vibrations are sensitive on the PID parameters in some ranges and the stable region will be decreased when considering the shafting system vibrations. The multi-objective PID parameter optimization strategy shows good control performance on the nonlinear dynamic characteristics of the HTGU. The shafting system vibrations excited by the coupled vibration sources are quasi-period in 3D space. In addition to this, these results and the optimization strategy can provide some bases for the design and stable operation of the HTGU.

scholarly journals IDENTIFICATION OF MATHEMATICAL MODEL OF TURBINE GENERATOR UNIT IN PRESENCE OF UNCERTAINTY

2021 ◽  
Vol 2021 (1) ◽  
pp. 32-39
Author(s):  
S.M. Baliuta ◽  
◽  
P.O. Chernenko ◽  
Iu.V. Kuievda ◽  
V.P. Kuevda ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Transfer Function ◽  
Automated System ◽  
Turbine Generator ◽  
Identification Procedure ◽  
Worst Case ◽  
Uncertainty Model ◽  
The Matrix ◽  
Generator Unit ◽  
Controlled Object

An identification procedure of mathematical model of turbine generator unit in the presence of uncertainty is studied for using in the interconnected robust control automated system. The procedure is based on “worst-case” identification approach. The controlled object is modelled by the matrix transfer function with additive uncertainty. The identification consists of two stages: first is to identify transfer function with nominal parameters with the use of prediction error minimization algorithm, second – to determine weight function in additive uncertainty model using finding the worst-case log-magnitude curve of uncertainties. Identification is performed in active way, determining datasets for each control channel from individual experiments. A linear frequency-modulated signal is selected as the input test disturbance. A simulation model of the controlled object is constructed and the numerical experiment is conducted using the identification procedure. References 11, figures 7.

Imprecision of Laboratory Determinations and Diagnostic Accuracy: Theoretical Considerations

1974 ◽  
Vol 13 (03) ◽  
pp. 151-158 ◽  
Author(s):  
D. A. B. Lindbebo ◽  
Fr. R. Watson
Keyword(s):  
Mathematical Model ◽  
Diagnostic Accuracy ◽  
Diagnostic Process ◽  
Clinical Laboratories ◽  
Non Linear ◽  
Theoretical Considerations ◽  
Made In

Recent studies suggest the determinations of clinical laboratories must be made more precise than at present. This paper presents a means of examining benefits of improvement in precision. To do this we use a mathematical model of the effect upon the diagnostic process of imprecision in measurements and the influence upon these two of Importance of Diagnosis and Prevalence of Disease. The interaction of these effects is grossly non-linear. There is therefore no proper intuitive answer to questions involving these matters. The effects can always, however, be calculated.Including a great many assumptions the modeling suggests that improvements in precision of any determination ought probably to be made in hospital rather than screening laboratories, unless Importance of Diagnosis is extremely high.

Glycemia monitoring

1998 ◽  
Vol 2 ◽  
pp. 23-30
Author(s):  
Igor Basov ◽  
Donatas Švitra
Keyword(s):  
Diabetes Mellitus ◽  
Mathematical Model ◽  
Numerical Methods ◽  
Differential Equations ◽  
Blood Sugar ◽  
Self Regulation ◽  
Physiological System ◽  
Non Linear ◽  
The Mathematical Model

Here a system of two non-linear difference-differential equations, which is mathematical model of self-regulation of the sugar level in blood, is investigated. The analysis carried out by qualitative and numerical methods allows us to conclude that the mathematical model explains the functioning of the physiological system "insulin-blood sugar" in both normal and pathological cases, i.e. diabetes mellitus and hyperinsulinism.

scholarly journals Set-membership identifiability of nonlinear models and related parameter estimation properties

2016 ◽  
Vol 26 (4) ◽  
pp. 803-813 ◽  
Author(s):  
Carine Jauberthie ◽  
Louise Travé-MassuyèEs ◽  
Nathalie Verdière
Keyword(s):  
Mathematical Model ◽  
Parameter Estimation ◽  
Dynamic System ◽  
Nonlinear Models ◽  
Differential Algebra ◽  
Related Parameter ◽  
Set Membership ◽  
Estimation Problems ◽  
The Mathematical Model

Abstract Identifiability guarantees that the mathematical model of a dynamic system is well defined in the sense that it maps unambiguously its parameters to the output trajectories. This paper casts identifiability in a set-membership (SM) framework and relates recently introduced properties, namely, SM-identifiability, μ-SM-identifiability, and ε-SM-identifiability, to the properties of parameter estimation problems. Soundness and ε-consistency are proposed to characterize these problems and the solution returned by the algorithm used to solve them. This paper also contributes by carefully motivating and comparing SM-identifiability, μ-SM-identifiability and ε-SM-identifiability with related properties found in the literature, and by providing a method based on differential algebra to check these properties.

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