Decoupling P-NARX models using filtered CPD

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.

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Identification of Hysteretic Systems Using NARX Models, Part I: Evolutionary Identification

Topics in Model Validation and Uncertainty Quantification, Volume 4 - Conference Proceedings of the Society for Experimental Mechanics Series ◽

10.1007/978-1-4614-2431-4_5 ◽

2012 ◽

pp. 49-56 ◽

Cited By ~ 9

Author(s):

K. Worden ◽

R. J. Barthorpe

Keyword(s):

Hysteretic Systems ◽

Narx Models

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Modeling and simulation of the start-up operation of an IPGT by using NARX models

Gas Turbines Modeling, Simulation, and Control ◽

10.1201/b18956-14 ◽

2015 ◽

pp. 142-159

Keyword(s):

Modeling And Simulation ◽

Start Up ◽

Narx Models

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An Ensemble of Recurrent Neural Networks for Real Time Performance Modelling of Three-Spool Aero-Derivative Gas Turbine Engine

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.4051112 ◽

2021 ◽

Author(s):

Ibrahem Mohamed Ibrahem ◽

Ouassima Akhrif ◽

Hany Moustapha ◽

Martin Staniszewski

Keyword(s):

Neural Networks ◽

Gas Turbine ◽

Gas Turbine Engine ◽

Performance Modelling ◽

Weighting Method ◽

Turbine Engine ◽

Single Output ◽

Operation Conditions ◽

Unseen Data ◽

Narx Models

Abstract Gas turbine is a complex system operating in non-stationary operation conditions for which traditional model-based modeling approaches have poor generalization capabilities. To address this, an investigation of a novel data-driven neural networks based model approach for a three-spool aero-derivative gas turbine engine (ADGTE) for power generation during its loading and unloading conditions is reported in this paper. For this purpose, a non-linear autoregressive network with exogenous inputs (NARX) is used to develop this model in MATLAB environment using operational closed-loop data collected from Siemens (SGT-A65) ADGTE. Inspired by the way biological neural networks process information and by their structure which changes depending on their function, multiple-input single-output (MISO) NARX models with different configurations were used to represent each of the ADGTE output parameters with the same input parameters. Usage of a single neural network to represent each of the system output parameters may not be able to provide an accurate prediction for unseen data and as a consequence, provides poor generalization. To overcome this problem, an ensemble of MISO NARX models is used to represent each output parameter. The major challenge of the ensemble generation is to decide how to combine results produced by the ensemble's components. In this paper, a novel hybrid dynamic weighting method (HDWM) is proposed. The simulation results show improvement in accuracy and robustness by using the proposed modeling approach.

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