<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Gas turbines are complex processes characterized by the instability and uncertainty of various sources. The range of useful operating in an axial compressor which is part of a turbine gas is limited by aerodynamic instabilities that are surge and rotating stall. This paper presents two intelligent fractional order sliding mode controllers. At first, a robust sliding fractional surface form is proposed to deal with hazardous phenomena which limit compression systems performance, and speed transitions, which can lead to temporary stall development, pressure drop at the output, degrade the effective operation of compressors and consequently gas turbines. Second, to reduce the chattering/fluctuation in control, a fuzzy logic and finite time criterion are used as switching control at the reaching phase in the sliding mode control. Additionally, the controller gains are obtained by offline multi-objective Particle swarm optimization (MOPSO) search. Finally, the surge and rotating stall of a Variable Speed Axial Compressor (VSAC) in a gas turbine are investigated under the system nonlinearities and also in presence of an external disturbance and perturbations. The simulation results signify the performance of the two MOPSO-based fractional sliding mode controllers.</p>
</abstract>

Gas turbines are complex processes characterized by the instability and uncertainty of various sources. The range of useful operating in an axial compressor which is part of a turbine gas is limited by aerodynamic instabilities that are surge and rotating stall. This paper presents two intelligent fractional order sliding mode controllers. At first, a robust sliding fractional surface form is proposed to deal with hazardous phenomena which limit compression systems performance, and speed transitions, which can lead to temporary stall development, pressure drop at the output, degrade the effective operation of compressors and consequently gas turbines. Second, to reduce the chattering/fluctuation in control, a fuzzy logic and finite time criterion are used as switching control at the reaching phase in the sliding mode control. Additionally, the controller gains are obtained by offline multi-objective Particle swarm optimization (MOPSO) search. Finally, the surge and rotating stall of a Variable Speed Axial Compressor (VSAC) in a gas turbine are investigated under the system nonlinearities and also in presence of an external disturbance and perturbations. The simulation results signify the performance of the two MOPSO-based fractional sliding mode controllers.

Gas Turbine Aerodynamics Improvement Via a Design of Intelligent Fractional Control

Strojnícky časopis - Journal of Mechanical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Gas turbines are complex processes characterized by the instability and uncertainty of various sources. The range of useful operating in an axial compressor...

Gas Turbine Aerodynamics Improvement Via a Design of Intelligent Fractional Control

Publié en ligne: 07 déc. 2021

Pages: 85 - 100

DOI: https://doi.org/10.2478/scjme-2021-0020

Mots clés
Gas turbine, axial compressor, surge, rotating stall, Fractional order sliding mode control (FOSMC), Multi-objectives Particle Swarm optimization (MOPSO)

© 2021 Debbah Abdesselam et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Gas Turbine Aerodynamics Improvement Via a Design of Intelligent Fractional Control

Publié en ligne: 07 déc. 2021

Pages: 85 - 100

DOI: https://doi.org/10.2478/scjme-2021-0020

Mots clésGas turbine, axial compressor, surge, rotating stall, Fractional order sliding mode control (FOSMC), Multi-objectives Particle Swarm optimization (MOPSO)

© 2021 Debbah Abdesselam et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Gas turbine, axial compressor, surge, rotating stall, Fractional order sliding mode control (FOSMC), Multi-objectives Particle Swarm optimization (MOPSO)