Monitoring Unsteady Thermal-Stress State of Large Power Steam Turbines based on Mathematical Modeling Semi-Centennial

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Research ID K4JQ8

Abstract

Transients of large power steam turbines with the single capacity of 250-300 MW and more should be run based mainly on the unsteady thermal-stress state of the high-temperature rotors monitored with the use of mathematical modeling by means of computing techniques at disposal under power plant’s real operational conditions. The problem was articulated and practical work started about half a century ago. Reasonable is to apply the same approach (as to the rotors) to monitoring unsteady thermal-stress state of the HP valve steam-chests what is especially essential for supercritical- and ultra-supercritical-pressure turbines. Special field studies as well as long-term practice of operation have confirmed that introducing operational monitoring of non-stationary temperature and thermal-stress states of the turbine at the transients makes it possible to identify operator errors that usually remain unnoticed with the traditional approach, creates conditions for improving the quality of operation and extending the turbine service life. The quality of monitoring based on mathematical modeling to a large extent depends on reliability and representativeness of primary heating steam temperature measurements. It should be expected thatin the nearest future under conditions of commissioning dominantly energy producers on renewable sources and the unresolved problem of accumulating the excess and covering the deficit of produced energy, a significant part of large fossil-fuel steam-turbine power units will be forced to operate in cyclic and standby modes what retains the actuality of the considered problem.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

Not applicable

Data Availability

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

Funding

This work did not receive any external funding.

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  • Classification

    DDC Code: 330.028 LCC Code: HB139

  • Version of record

    v1.0

  • Issue date

    27 September 2022

  • Language

    English

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