Identification of twin-shaft gas turbine based on hybrid decoupled state multiple model approach

Monitoring control of industrial systems is essential for the good productivity and safety of installations and operators, with better performance that must be guaranteed. This is often challenging due to the nonlinearities and dynamic complexities of these systems, adding operating constraints and...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2023-11, Vol.27 (22), p.17267-17289
Main Authors: Aissat, Sidali, Iratni, Abdelhamid, Hafaifa, Ahmed, Guemana, Mouloud, Alshammari, Obaid S., Colak, Ilhami
Format: Article
Language:English
Subjects:
Application of Soft Computing
Artificial Intelligence
Behavior
Computational Intelligence
Constraint modelling
Control
Convergence
Cost function
Dynamic stability
Engineering
Gas turbines
Genetic algorithms
Identification
Mathematical Logic and Foundations
Mathematical models
Mechatronics
Neural networks
Nonlinear systems
Nonlinearity
Optimization
Real time
Robotics
Systems stability
Turbines
Variables
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Summary:Monitoring control of industrial systems is essential for the good productivity and safety of installations and operators, with better performance that must be guaranteed. This is often challenging due to the nonlinearities and dynamic complexities of these systems, adding operating constraints and instability. Hence, the multi-models constitute then an adapted tool for the modeling of the nonlinear systems to characterize their dynamic behaviors. Indeed, this work proposes the implementation of a hybrid identification approach of the operating variables of a gas turbine, thus making it possible to interconnect the various linear sub-models with decoupled states in order to generate the global output of their nonlinear model, from the exploitation in real time of the turbine's input/output data. However, the suggested decoupled-state multi-model approach offers an interesting alternative to the optimization procedure of the estimated turbine parameters. By using gradient and Gauss–Newton algorithms, improved by genetic algorithms combined with NSGA-II in hybrid form, in order to converge toward the best solutions with an optimal cost function, the obtained implementation results show that this approach allows the convergence of the estimated turbine variables and describes its behavior in real time, with the guaranteed efficiency of the proposed decoupled state multi-model method.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08059-2