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Estimating Gas Turbine Internal Cycle Parameters Using a Neural Network FREE

[+] Author Affiliations
Nicolas W. Chbat, Ravi Rajamani

GE Corporate Research & Development, Schenectady, NY

Todd A. Ashley

GE Power Generation Engineering, Schenectady, NY

Paper No. 96-GT-316, pp. V005T15A023; 5 pages
doi:10.1115/96-GT-316
From:
  • ASME 1996 International Gas Turbine and Aeroengine Congress and Exhibition
  • Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; General
  • Birmingham, UK, June 10–13, 1996
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-7876-7
  • Copyright © 1996 by ASME

abstract

We show that a neural network can be successfully used in place of an actual model to estimate key unmeasured parameters in a gas turbine. As an example we study the combustion reference temperature, a parameter that is currently estimated via a nonlinear model inside the controller and is used in a number of critical mode-setting functions within the controller such as calculating the fuel-split between various manifolds. We show that a feedforward neural network using simple back propagation learning can be built for estimating combustion reference temperature. The neural network matches the accuracy of the current estimate; and it is more robust to errors in its internal parameters. This is advantageous from the point of view of implementation since a number of errors creep in when running the algorithm on a digital controller, and an estimator that is not robust with respect to such errors can degrade the performance of the whole system.

Copyright © 1996 by ASME
This article is only available in the PDF format.

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