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Research Papers: Gas Turbines: Turbomachinery

Online Prediction of Temperature and Stress in Steam Turbine Components Using Neural Networks

[+] Author and Article Information
Krzysztof Dominiczak

ALSTOM Power Ltd.,
ul. Stoczniowa 2,
Elblag 82-300, Poland
e-mail: krzysztof.dominiczak@power.alstom.com

Romuald Rządkowski

Institute of Fluid Flow Machinery of PASc,
ul. Fiszera 14,
Gdansk 80-231, Poland
e-mail: z3@imp.gda.pl

Wojciech Radulski

ALSTOM Power Ltd.,
ul. Stoczniowa 2,
Elblag 82-300, Poland
e-mail: wojciech.radulski@power.alstom.com

Ryszard Szczepanik

Air Force Institute of Technology,
ul. Księcia Bolesława 6,
P.O. Box 96,
Warszawa 01-494, Poland
e-mail: ryszard.szczepanik@itwl.pl

1Corresponding author.

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received August 5, 2015; final manuscript received September 6, 2015; published online November 11, 2015. Editor: David Wisler.

J. Eng. Gas Turbines Power 138(5), 052606 (Nov 11, 2015) (13 pages) Paper No: GTP-15-1396; doi: 10.1115/1.4031626 History: Received August 05, 2015; Revised September 06, 2015

Considered here are nonlinear autoregressive neural networks (NETs) with exogenous inputs (NARX) as a mathematical model of a steam turbine rotor used for the online prediction of turbine temperature and stress. In this paper, the online prediction is presented on the basis of one critical location in a high-pressure (HP) steam turbine rotor. In order to obtain NETs that will correspond to the temperature and stress the critical rotor location, a finite element (FE) rotor model was built. NETs trained using the FE rotor model not only have FEM accuracy but also include all nonlinearities considered in an FE model. Simultaneous NETs are algorithms which can be implemented in turbine controllers. This allows for the application of the NETs to control steam turbine stress in industrial power plants.

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References

Figures

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Fig. 2

HP cross section of 18K390 steam turbine

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Fig. 3

NARX NET-based steam turbine thermal stress control

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Fig. 4

matlab implementation of NARX NET responsible assessment of critical point stress: (a) parallel architecture and (b) series–parallel architecture

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Fig. 5

NETs structure optimization results: (a) temperature and (b) stress

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Fig. 6

Temperature before HP steam path during turbine warm start II

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Fig. 7

Heat transfer coefficient for rotor surface after first HP stationary blades row for various local steam temperatures, local steam pressures at nominal steam flow and nominal rotational speed

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Fig. 8

FE model of 18K390 turbine HP rotor: (a) whole model, (b) steam inlet view, and (c) first and second blade grooves

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Fig. 9

HP rotor axial expansion during CS: calculations and measurements

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Fig. 10

NET performance based on real turbine operating data

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Fig. 11

Result of NET tests for CS

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Fig. 12

Result of NET tests for LR and turbine reloading

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Fig. 13

Result of NET tests for standstill after turbine SD with steam cooling phase

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Fig. 14

NET prediction performance based on real turbine operating data: (a) temperature and (b) stress

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Fig. 15

Temperature prediction in the 54th minute of warm start II

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Fig. 16

Stress prediction in the 54th minute of warm start II

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Fig. 17

Temperature prediction in the 55th minute of warm start II

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Fig. 18

Stress prediction in the 55th minute of warm start II

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Fig. 19

Temperature prediction in the 61st minute of warm start II

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Fig. 20

Stress prediction in the 61st minute of warm start II

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Fig. 21

Temperature prediction in the 62nd minute of warm start II

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Fig. 22

Stress prediction in the 62nd minute of warm start II

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Fig. 23

Temperature prediction just before turbine LR, i.e., 68th minute

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Fig. 24

Stress prediction just before turbine LR, i.e., 68th minute

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Fig. 25

Temperature prediction after turbine LR, i.e., 69th minute

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Fig. 26

Stress prediction after turbine LR, i.e., 69th minute

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