Modeling and Simulation of the Start-Up Operation of a Heavy-Duty Gas Turbine by Using NARX Models

Asgari, Hamid; Chen, Xiaoqi; Sainudiin, Raazesh; Morini, Mirko; Pinelli, Michele; Spina, Pier Ruggero; Venturini, Mauro

doi:10.1115/gt2014-25056

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…NARX neural networks have been used for dynamic modelling of reactor-exchangers [22], crude preheater [23], hydraulic suspension dampers [24], unsteady separation control [25], gas turbines [26,27], magnetic levitation [28] among others. There is yet to be a case of data-driven drum-boiler models based on NARX neural networks to the best of our knowledge.…”

Section: Aims and Noveltymentioning

confidence: 99%

Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant

Oko

Wang

Zhang

2015

Fuel

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Keywords:NARX neural networks Subcritical coal-fired power plant Drum-boiler gPROMS modelling and simulation a b s t r a c t There is increasing need for tighter controls of coal-fired plants due to more stringent regulations and addition of more renewable sources in the electricity grid. Achieving this will require better process knowledge which can be facilitated through the use of plant models. Drum-boilers, a key component of coal-fired subcritical power plants, have complicated characteristics and require highly complex routines for the dynamic characteristics to be accurately modelled. Development of such routines is laborious and due to computational requirements they are often unfit for control purposes. On the other hand, simpler lumped and semi empirical models may not represent the process well. As a result, data-driven approach based on neural networks is chosen in this study. Models derived with this approach incorporate all the complex underlying physics and performs very well so long as it is used within the range of conditions on which it was developed. The model can be used for studying plant dynamics and design of controllers. Dynamic model of the drum-boiler was developed in this study using NARX neural networks. The model predictions showed good agreement with actual outputs of the drum-boiler (drum pressure and water level).

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Section: Aims and Noveltymentioning

confidence: 99%

Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant

Oko

Wang

Zhang

2015

Fuel

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“…In this step, a dataset is determined on the input and output variables identified in step P1 . The dataset can be obtained by simulation [ 29 ] or measuring the GT under study [ 30 ]. In either case, it is required that data cover one operating cycle of the GT, that the period between data recordings is constant and as small as a few minutes or seconds, and that data are reliable.…”

Section: Systematic Methods To Build the Narx Modelmentioning

confidence: 99%

System Identification Methodology of a Gas Turbine Based on Artificial Recurrent Neural Networks

Aquize

Cajahuaringa

Machuca

et al. 2023

Sensors

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The application of identification techniques using artificial intelligence to the gas turbine (GT), whose nonlinear dynamic behavior is difficult to describe through differential equations and the laws of physics, has begun to gain importance for a little more than a decade. NARX (Nonlinear autoregressive network with exogenous inputs) is one of the models used to identify GT because it provides good results. However, existing studies need to show a systematic method to generate robust NARX models that can identify a GT with satisfactory accuracy. In this sense, a systematic method is proposed to design NARX models for identifying a GT, which consists of nine precise steps that go from identifying GT variables to obtaining the optimized NARX model. To validate the method, it was applied to a case study of a 215 MW SIEMENS TG, model SGT6-5000F, using a set of 2305 real-time series data records, obtaining a NARX model with an MSE of 1.945 × 10−5, RMSE of 0.4411% and a MAPE of 0.0643.

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“…In this study, physical based model has been developed in MATLAB/Simulink while transient model has been established using the NARX approach by employing same operational data set. Lately, this transient model was expanded to the startup operation of single shaft heavy duty industrial gas turbine being operating in Italy [ 125 ]. It can be inferred that, NARX approach can only be utilized for developing models of the engines with available operational data.…”

Section: Methods and Techniques For Transient Modelsmentioning

confidence: 99%

Transient Behavior in Variable Geometry Industrial Gas Turbines: A Comprehensive Overview of Pertinent Modeling Techniques

Hashmi

Lemma

Ahsan

et al. 2021

Entropy

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Generally, industrial gas turbines (IGT) face transient behavior during start-up, load change, shutdown and variations in ambient conditions. These transient conditions shift engine thermal equilibrium from one steady state to another steady state. In turn, various aero-thermal and mechanical stresses are developed that are adverse for engine’s reliability, availability, and overall health. The transient behavior needs to be accurately predicted since it is highly related to low cycle fatigue and early failures, especially in the hot regions of the gas turbine. In the present paper, several critical aspects related to transient behavior and its modeling are reviewed and studied from the point of view of identifying potential research gaps within the context of fault detection and diagnostics (FDD) under dynamic conditions. Among the considered topics are, (i) general transient regimes and pertinent model formulation techniques, (ii) control mechanism for part-load operation, (iii) developing a database of variable geometry inlet guide vanes (VIGVs) and variable bleed valves (VBVs) schedules along with selection framework, and (iv) data compilation of shaft’s polar moment of inertia for different types of engine’s configurations. This comprehensive literature document, considering all the aspects of transient behavior and its associated modeling techniques will serve as an anchor point for the future researchers, gas turbine operators and design engineers for effective prognostics, FDD and predictive condition monitoring for variable geometry IGT.

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Modeling and Simulation of the Start-Up Operation of a Heavy-Duty Gas Turbine by Using NARX Models

Cited by 9 publications

References 31 publications

Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant

Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant

System Identification Methodology of a Gas Turbine Based on Artificial Recurrent Neural Networks

Transient Behavior in Variable Geometry Industrial Gas Turbines: A Comprehensive Overview of Pertinent Modeling Techniques

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