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

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Abstract

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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Abbreviations

ANN:

Artificial neural network

BP:

Back propagation

DEH:

Digital electric-hydraulic control system

GA:

Genetic algorithm

NN:

Neural network

PFC:

Primary frequency control

PSO:

Particle swarm optimization

RBF:

Radial basis function

UHV:

Ultra-high voltage

UHVTT:

Ultra-high voltage transmission technology

d 1 :

Artificial neural network

d 2 :

Back propagation

f t :

Digital electric-hydraulic control system

f z :

Genetic algorithm

l 1 :

Neural network

k 2 :

Primary frequency control

l 1 :

Particle swarm optimization

l 2 :

Radial basis function

n :

Ultra-high voltage

s :

Ultra-high voltage transmission technology

T :

Primary frequency control

T y :

Particle swarm optimization

T c :

Radial basis function

x :

Ultra-high voltage

x min :

Ultra-high voltage transmission technology

x max :

Ultra-high voltage transmission technology

x* :

Ultra-high voltage transmission technology

τ :

Time constant of delay rate(s)

γ :

Parameter vector

α :

Output number of neural network

β :

Input number of neural network

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Acknowledgements

This research was supported by the Electric Power Research Institute of State Grid Corporation of China in Zhejiang province.

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Authors and Affiliations

  1. Institute of Thermal Science and Power Systems, Zhejiang University, Hangzhou, 310027, China

    Jin-Long Liao, De-Ren Sheng & Zi-Tao Yu

  2. Electric Power Research Institute of State Grid Zhejiang Electric Power Company, Hangzhou, 310027, China

    Feng Yin, Zhi-Hao Luo & Bo Chen

Authors
  1. Jin-Long Liao
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  2. Feng Yin
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  3. Zhi-Hao Luo
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  4. Bo Chen
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  5. De-Ren Sheng
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  6. Zi-Tao Yu
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Corresponding author

Correspondence to Zi-Tao Yu.

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Technical Editor: Jose A. dos Reis Parise.

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Liao, JL., Yin, F., Luo, ZH. et al. The parameter identification method of steam turbine nonlinear servo system based on artificial neural network. J Braz. Soc. Mech. Sci. Eng. 40, 165 (2018). https://doi.org/10.1007/s40430-018-1086-8

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  • DOI: https://doi.org/10.1007/s40430-018-1086-8

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