Application of Recursive Least Squares Method for Parameter Identification of DEH Governing System

Li, Wei; Chun-lin, Meng; De-ren, Sheng; Chen, Jianhong; Hao-ren, Ren

doi:10.1007/978-3-540-76694-0_52

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…The simulation results of valve opening were not accurate in [5], the main mentioned error comes from the nonlinear factors by theoretical analysis. In [6], ANN was used to model steam valve servo system and turbine system including generator.…”

Section: Introductionmentioning

confidence: 98%

The parameter identification method of steam turbine nonlinear servo system based on artificial neural network

Liao

Yin

Zhihao

et al. 2018

J Braz. Soc. Mech. Sci. Eng.

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The servo system in steam turbine digital electric-hydraulic control system (DEH) is affected by nonlinear factors when it is working. To accurately simulate dynamic characteristics of the DEH, a new nonlinear servo system is proposed, which has limit, dead zone and correction coefficient caused by unknown factors. The model parameters are divided into linear parameters and nonlinear parameters to be identified, respectively. Neural networks are used to identify linear parameters. The nonlinear parameters should be identified according to flow characteristic curve. To verify the validity of the proposed model and parameter identification method, the actual data of primary frequency control from a 1000 MW Ultra Supercritical Unit is adopted. Meanwhile, the linear model with no nonlinear factors is used for comparison. Where the fitting degree of valve opening is 98.445% and power is 96.986%, the output of nonlinear model coincides with actual output well. Where the relative error of stable result is 5% of valve opening and 1.58% of power, the error of linear model is larger. The simulation results of the proposed method show that the nonlinear factors of high-power units cannot be ignored and the nonlinear model of servo system is more accurate.

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Section: Introductionmentioning

confidence: 98%

The parameter identification method of steam turbine nonlinear servo system based on artificial neural network

Liao

Yin

Zhihao

et al. 2018

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…The time constant are estimated by means of methods based on Recursive Least Squares [19], Genetic Algorithm [20], Particle Swarm Optimization [21], Bacterial Foraging Optimization Algorithm [22] and Extended Kalman Filter [23]. In the literature, non-parametric methods, such as Artificial Neural Network, have been used to estimated and model the components of the LFC [24,25].…”

Section: Introductionmentioning

confidence: 99%