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research-article

DEVELOPMENT OF RELIABLE NARX MODELS OF GAS TURBINE COLD, WARM AND HOT START-UP

[+] Author and Article Information
Hilal Bahlawan

Dipartimento di Ingegneria, Università degli Studi di Ferrara, Ferrara, Italy
hilal.bahlawan@unife.it

Mirko Morini

Dipartimento di Ingegneria e Architettura, Università degli Studi di Parma, Parma, Italy
mirko.morini@unipr.it

Michele Pinelli

Dipartimento di Ingegneria, Università degli Studi di Ferrara, Ferrara, Italy
michele.pinelli@unife.it

Pier Ruggero Spina

Dipartimento di Ingegneria, Università degli Studi di Ferrara, Ferrara, Italy
pier.ruggero.spina@unife.it

Mauro Venturini

Dipartimento di Ingegneria, Università degli Studi di Ferrara, Ferrara, Italy
mauro.venturini@unife.it

1Corresponding author.

ASME doi:10.1115/1.4038838 History: Received November 16, 2017; Revised November 18, 2017

Abstract

This paper documents the set-up and validation of nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine. The data used for model training are time series datasets of several different maneuvers taken experimentally on a gas turbine General Electric PG 9351FA during the start-up procedure and refer to cold, warm and hot start-up. The trained NARX models are used to predict other experimental datasets 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 datasets 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 (c) 2017 by ASME
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