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Development of Reliable NARX Models of Gas Turbine Cold, Warm and Hot Start-Up

[+] Author Affiliations
Hilal Bahlawan, Michele Pinelli, Pier Ruggero Spina, Mauro Venturini

Università degli Studi di Ferrara, Ferrara, Italy

Mirko Morini

Università degli Studi di Parma, Parma, Italy

Paper No. GT2017-63332, pp. V009T27A007; 14 pages
  • ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition
  • Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5096-1
  • Copyright © 2017 by ASME


This paper documents the set-up and validation of nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine. The considered gas turbine is a General Electric PG 9351FA located in Italy.

The data used for model training are time series data sets of several different maneuvers taken experimentally during the start-up procedure and refer to cold, warm and hot start-up. The trained NARX models are used to predict other experimental data sets and comparisons are made among the outputs of the models and the corresponding measured data.

Therefore, this paper addresses the challenge of setting up robust and reliable NARX models, by means of a sound selection of training data sets and a sensitivity analysis on the number of neurons. Moreover, a new performance function for the training process is defined to weigh more the most rapid transients. The final aim of this paper is the set-up of a powerful, easy-to-build and very accurate simulation tool which can be used for both control logic tuning and gas turbine diagnostics, characterized by good generalization capability.

Copyright © 2017 by ASME
Topics: Gas turbines



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