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

Bahlawan, Hilal; Morini, Mirko; Pinelli, Michele; Spina, Pier Ruggero; Venturini, Mauro

doi:10.1115/gt2017-63332

Cited by 7 publications

(8 citation statements)

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“…They built the NARX model using the data calculated using the physicsbased model and veri ed it through dynamic simulation. Bahlawan et al [25] applied a NARX method to create a model for predicting start-up performance according to ambient temperature conditions in a single-shaft industrial gas turbine. Salehi and Montazeri [26] presented a black box modeling approach based on a NARX structure to model accurately the fuel control unit in a turboshaft engine.…”

Section: Introductionmentioning

confidence: 99%

A data-driven modeling method for an aero gas turbine engine system based on the transient data

Kim

2022

Preprint

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Accurate performance prediction models in gas turbines are essential for diagnosis. In this paper, a data-driven modeling method is newly proposed for an aero gas turbine engine system with high prediction accuracy during dynamic operation by considering the time delay included in the measured temperature from a sensor. Modeling and analysis are performed using an aero gas turbine engine with rapid thrust change. The accuracy of the data-driven model trained using the measured temperature and the flow temperature calculated through physical relations is compared. The flow temperature is calculated from the measured temperature based on lumped system analysis. In data learning, the low-pressure shaft speed and low-pressure turbine total temperature are defined as input parameters, whilst engine net thrust, and fuel mass flow rate are defined as output ones. The results show that using the measured temperature for training without conversion adversely affects the accuracy of the data-driven model. The flow temperature converted from the measured values is reasonable in terms of thermodynamic cycle matching of the engine. It is confirmed that the accuracy of the data-driven model is significantly improved when the actual flow temperature is used for model training.

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Section: Introductionmentioning

confidence: 99%

A data-driven modeling method for an aero gas turbine engine system based on the transient data

Kim

2022

Preprint

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“…Non linear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network. (Salehi & Montazeri-Gh 2018, Asgari et al 2016, Bahlawan et al 2017 used NARX neural network to model ADGTE.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the literature survey, in the area of MIMO ANN modelling of gas turbine engines, the research activities used mostly one of the following two methods to generate a nonlinear model for the MIMO engine: Either, by building a neural network model for each output parameter (MISO) with the same structure for each one of them and trained with the same training algorithm (Bahlawan et al 2017, Asgari et al 2016, or by building one block neural model to represent the MIMO system (Asgari et al 2014, Tarik et al 2017, Salehi & Montazeri-Gh 2018, Ibrahem et al 2019. However, it is more powerful, as will be shown in this study, to use a different neural network's structure for each output.…”

Section: Introductionmentioning

confidence: 99%

Real time modeling and Hardware in the loop simulation of an aero-derivative gas turbine engine

Ibrahem¹,

Akhrif²,

Moustapha³

et al. 2020

Proceedings of Global Power &Amp; Propulsion Society

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Modelling and simulation of gas turbines plays a key role in manufacturing and improving performance of gas turbine engines. In recent years, the importance of ADGTEs in the energy industry has sparked a great interest among manufacturers to improve the performance and increase the reliability of the engine, which in turn requires an accurate and real time model to simulate the engine dynamics during the full operating range. For this purpose, a real-time modelling and hardware in the loop (HIL) simulation of an aero-derivative gas turbine engine (ADGTE) are developed in this paper. 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) three-spool dry low emission ADGTE. A multiple-input single-output (MISO) NARX models with different configurations are used to represent each of the ADGTE output parameters with the same input parameters. First, data preprocessing and estimation of the order of these MISO models are performed. Next, a comprehensive computer program code is developed to perform a comparative study and to select the best NARX model configuration, which can represent the system dynamics. Finally, to demonstrate the performance of the MISO NARX model developed in this study, a HIL simulation is performed. In this simulation, the physical ADGTE is replaced with the MISO NARX model, and the physical engine controller is used to control that model. In addition, the neural model is validated with experimental data. The simulation results show that the proposed MISO NARX models followed the targets precisely and can predict the response with high accuracy and reliability. In addition, the proposed model presents high computation speed, which allows for real time applications.

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“…The intermittent nature of renewables prompts the gas turbines to operate with increased flexibility, for supporting their renewable plant partners and maintaining the stability of the electricity grid [1,2]. Fast start up, shut down, load following modes, and part load operation [3,4,5] are dominating the operating regime of today's gas turbines. Understanding the behavior of these engines, under such demanding operating conditions, is crucial for their successful operation and maintenance (O&M).…”

Section: Introductionmentioning

confidence: 99%

Dynamic performance simulation and control of gas turbines used for hybrid gas/wind energy applications

Tsoutsanis

Meskin

2019

Applied Thermal Engineering

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

Cited by 7 publications

References 0 publications

A data-driven modeling method for an aero gas turbine engine system based on the transient data

A data-driven modeling method for an aero gas turbine engine system based on the transient data

Real time modeling and Hardware in the loop simulation of an aero-derivative gas turbine engine

Dynamic performance simulation and control of gas turbines used for hybrid gas/wind energy applications

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