Model reduction and MIMO model predictive control of gas turbine systems

Wiese, Ashley; Blom, Matthew J.; Manzie, Chris; Brear, Michael J.; Kitchener, Anthony

doi:10.1016/j.conengprac.2015.09.015

Cited by 47 publications

(23 citation statements)

References 26 publications

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“…In order to overcome this complexity and reduce the computational burden, it is very much essential to identify an equivalent reduced‐order model, which would retain the dominant characteristics of the original system . It is understood from the literature review that only few attempts were made to identify the reduced‐order model of the real‐time system, viz, wind turbine blade system and micro‐turbine system . Though the previous researchers used the Rowen (1983) model only for dynamic analysis and controller development for HDGT‐, there were no attempts made to identify the reduced‐order model of the HDGT plant.…”

Section: Introductionmentioning

confidence: 99%

“…17 It is understood from the literature review that only few attempts were made to identify the reduced-order model of the real-time system, viz, wind turbine blade system and micro-turbine system. 18,19 Though the previous researchers used the Rowen (1983) model only for dynamic analysis and controller development for HDGT 3,8,10,12 -, 15,20 there were no attempts made to identify the reduced-order model of the HDGT plant. For the dynamic studies of HDGT with less computation, it is essential to develop the reduced-order model.…”

Section: Introductionmentioning

confidence: 99%

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Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Iqbal

Xavier²

2020

Int Trans Electr Energ Syst

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Summary Analysis of higher‐order gas turbine plant in real time would be tedious and expensive. In order to overcome this complexity, reduced‐order model for 5001M heavy‐duty gas turbine rated 18.2 MW has been obtained by Routh approximation, clustering technique, modified pole clustering, eigen permutation, Mihailov criterion, and Padé approximation algorithms. The step responses are obtained using MATLAB/Simulink and compared based on time domain specifications and performance index criteria. It indicates that the mixed method, namely, Routh approximation–Padé approximation algorithm–based reduced‐order model, retains the original characteristics. Further, particle swarm optimization (PSO) algorithm has also been applied to develop an optimal reduced‐order model. Based on the dynamic response against the load disturbance and set point variations, PSO‐based reduced‐order model has been identified as an optimal reduced‐order model for heavy‐duty gas turbine. The reduced‐order model proposed in this paper will be suitable for analyzing the dynamic behavior of heavy‐duty gas turbine plants in real‐time environment.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Iqbal

Xavier²

2020

Int Trans Electr Energ Syst

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“…Analysis of higher order gas turbine plant in real‐time environment would be tedious and expensive. Further, the gas turbine control would be impossible in real time due to the model complexity and computational complexity . Even though some researchers have attempted for model order reduction of larger gas turbine system, the same procedure may not be extended for all turbines .…”

Section: Introductionmentioning

confidence: 99%

“…Further, the gas turbine control would be impossible in real time due to the model complexity and computational complexity. 22 Even though some researchers have attempted for model order reduction of larger gas turbine system, the same procedure may not be extended for all turbines. 23 The higher order models would be impractical for the system studies due to system size and computational burden.…”

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confidence: 99%

Model order reduction of heavy duty gas turbine power plants with field test parameters

Iqbal

Sarumathi²,

Jothi

et al. 2018

Int Trans Electr Energ Syst

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Summary Frequent load fluctuation and set point variations are the major issues in grid connected heavy duty gas turbine power plants. It may affect the stability of the plant and may lead to inevitable shutdown. Dynamic stability analysis of higher order heavy duty gas turbine in real‐time environment would be tedious and expensive. Various model order reduction techniques namely clustering technique‐Pade approximation (CT‐PA), modified clustering technique‐Pade approximation (MCT‐PA), and Mihailov criterion‐Pade approximation (MC‐PA) are proposed for identifying the reduced order model of 18.2 MW industrial heavy duty gas turbine with field test parameter. The step response of gas turbine plant against the load disturbance and set point variations is obtained using the reduced order models and compared with that of higher order system. The time domain specifications and error criteria have witnessed that the MC‐PA‐based reduced order model retains the original characteristics and yields an improved transient and steady‐state responses. Therefore, MC‐PA‐based reduced order model is identified as an effective reduced order model for the higher order gas turbine plant. The proposed reduced order model would be useful for analysing the behaviour of latest derivative speedtronic controlled gas turbine plant.

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“…The goal of this chapter is to represent the state-space model of the turbojet engine, with significant constraints applied to the real object, in order to allow less difficult approach of the control, as proposed by Wiese, Blom, Manzie, Brear and Kitchener (2015).…”

Section: Developing the State-space Model Of Turbojet Engine 21 Statmentioning

confidence: 99%

Linear Mathematical Model for State-Space Representation of Small Scale Turbojet Engine with Variable Exhaust Nozzle

Beneda

Andoga

Főző

2017

Period. Polytech. Transp. Eng.

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Model reduction and MIMO model predictive control of gas turbine systems

Cited by 47 publications

References 26 publications

Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Model order reduction of heavy duty gas turbine power plants with field test parameters

Linear Mathematical Model for State-Space Representation of Small Scale Turbojet Engine with Variable Exhaust Nozzle

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