Nonlinear Gas Turbine Modeling Using Feedforward Neural Networks

Chiras, Neophytos; Evans, C.; Rees, D.

doi:10.1115/gt2002-30035

Cited by 36 publications

(21 citation statements)

References 13 publications

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“…It can be understood from Figs. 3, 4 that a second-order structure having least NSSE [23] provides the best fit on the existing validation data. Subsequently a second-order structure with single hidden neuron results in the least value for the NSSE.…”

Section: Network Structurementioning

confidence: 99%

Small hydro power plant identification using NNARX structure

Kishor

Saini

Singh

2005

Neural Comput & Applic

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A feedforward multi-layer perceptron neural network structure is developed to model the nonlinear dynamic relationship between input and output of a hydro power plant connected as single machine infinite bus system. Two independent second-order neural network nonlinear auto-regressive with exogenous signal models are used in the study. The structure selection of each independent model is based on various validation tests. The optimal brain surgeon pruning strategy adopted for optimizing the neural network structure. The network performance is studied for fixed and change in operating point.

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Section: Network Structurementioning

confidence: 99%

Small hydro power plant identification using NNARX structure

Kishor

Saini

Singh

2005

Neural Comput & Applic

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“…The simulation results showed the effectiveness of the estimated NARMAX model for small and large signal ranges in engine tests. In other papers, they proposed a mathematical relationship between the fuel flow rate and the shaft speed of a gas turbine engine taking into account the system as a black box using NN and NARMAX models [3,4]. Nonparametric and parametric models of an aircraft engine (type: XTE46) were presented by Maggiore et al [11].…”

Section: Introductionmentioning

confidence: 98%

Parametric and nonparametric system identification of an experimental turbojet engine

Tavakolpour-Saleh

Nasib

Sepasyan

et al. 2015

Aerospace Science and Technology

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“…The system to be investigated is a gas turbine. Various approaches such as Fuzzy [8], MPC [9], and Neural Network [10] are used for control of different types of this system. The aim of the present paper is to design an optimal LQG/LTR controller for this system.…”

Section: Introductionmentioning

confidence: 99%

Using LQG/LTR Optimal Control Method to Improve Stability and Performance of Industrial Gas Turbine System

Shabaninia

Jafari

2012

ISRN Electronics

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The gas turbine is a power plant, which produces a great amount of energy for its size and weight. Its compactness, low weigh, and multiple fuels make it a natural power plant for various industries such as power generation or oil and gas process plants. In any of these applications, the performance and stability of the gas turbines are the end products that strongly influence the profitability of the business that employs them. Control and analyses of gas turbines for achieving stability and good performance are important so that they have to operate for prolong period. Effective control system design usually benefits from an accurate dynamic model of the plant. Characteristic component parts of the system are considered in this model. Gas turbine system is described by specified thermodynamic equations that can be used for defining its model. This paper introduces an optimal LQG/LTR control method for a gas turbine. Analysing the gas turbine dynamic in time and frequency domain by using proposed control compared to PID controller is followed. Applying this optimal control method can provide good performance and stability for the component parts of system.

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Nonlinear Gas Turbine Modeling Using Feedforward Neural Networks

Cited by 36 publications

References 13 publications

Small hydro power plant identification using NNARX structure

Small hydro power plant identification using NNARX structure

Parametric and nonparametric system identification of an experimental turbojet engine

Using LQG/LTR Optimal Control Method to Improve Stability and Performance of Industrial Gas Turbine System

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