Improving the Performance of Gas Turbine based on ‎Rowen Model Using Type-2 Fuzzy Controller‎

Document Type : Original Article


1 Department of Electrical Engineering, Semnan Branch, Islamic ‎Azad University, Semnan, Iran‎

2 Energy and Sustainable Development Research Center, Semnan ‎Branch, Islamic Azad University, Semnan, Iran‎

3 Department of Electrical Engineering, East Tehran Branch, Islamic ‎Azad University, Tehran , Iran‎


According to the crucial role of gas turbines in electricity production without significant harmful effects on the environment, this paper is aimed at the modeling and simulation of a particular type of these systems known as V94.2. Gas turbine is an instrument for power generation, which is capable of producing a vast amount of energy by considering its size and weight. Despite all the advantages and applications of gas turbines, the use of these systems is not free of difficulties because of their remote controls in such a way that it is estimated that about a quarter of turbine price is spent on its launching. To tackle this problem, a mathematical model has been proposed for V94.2 gas turbines as a result of the review of the pieces of research done on the modelling of gas turbines in the past few years and on the basis of Rowen model. In the following, the capability of fuzzy type-2 controllers has been sed to ensure the system stability. The results of the simulation in MATLAB software clearly shows that the output variables of V94.2 gas turbine reach a specific situation and place after applying the inputs at the right time.


[1] S. Baudoin, I. Vechiu, and H. Camblong, “A review of voltage and frequency control strategies for islanded microgrid,” in System Theory, Control and Computing (ICSTCC), 2012 16th International Conference on, pp. 1–5, 2012.
[2] S. O. Oyedepo, R. O. Fagbenle, S. S. Adefila, and S. A. Adavbiele, “Performance evaluation and economic analysis of a gas turbine power plant in Nigeria,” Energy Convers. Manag., vol. 79, pp. 431–440, 2014.
[3] A. Buonomano, F. Calise, M. D. d’Accadia, A. Palombo, and M. Vicidomini, “Hybrid solid oxide fuel cells–gas turbine systems for combined heat and power: A review,” Appl. Energy, vol. 156, pp. 32–85, 2015.
[4] T. Addabbo, O. Cordovani, A. Fort, M. Mugnaini, V. Vignoli, and S. Rocchi, “Gas Turbine Thermoelements Availability Analysis,” in Sensors, Springer, 2015, pp. 387–391.
[5] E. Khorasani Nejad,  F. Hajabdollahi, Z. Hajabdollahi, and H. Hajabdollahi, “Thermo-economic Optimization of Gas Turbine Power Plant with Details in Intercooler,” Heat Transfer—Asian Res., vol. 42, no. 8, pp. 704–723, 2013.
[6] A. Mehrpanahi and G. H. Payganeh, “Multi-objective optimization of IGV position in a heavy-duty gas turbine on part-load performance,” Appl. Therm. Eng., vol. 125, pp. 1478–1489, 2017.
[7] S. Borguet and O. Léonard, “Comparison of adaptive filters for gas turbine performance monitoring,” J. Comput. Appl. Math., vol. 234, no. 7, pp. 2202–2212, 2010.
[8] S. S. Tayarani-Bathaie and K. Khorasani, “Fault detection and isolation of gas turbine engines using a bank of neural networks,” J. Process Control, vol. 36, pp. 22–41, 2015.
[9] S. K. Yee, J. V. Milanović, and F. M. Hughes, “Overview and comparative analysis of gas turbine models for system stability studies,” Power Syst. IEEE Trans. On, vol. 23, no. 1, pp. 108–118, 2008.
[10]    S. Simani, C. Fantuzzi, and R. J. Patton, Model-based fault diagnosis in dynamic systems using identification techniques. Springer Science & Business Media, 2013.
[11] W. I. Rowen, “Simplified mathematical representations of single shaft gas turbines in mechanical drive service,” in ASME 1992 International Gas Turbine and Aeroengine Congress and Exposition, 1992, p. V005T15A001–V005T15A001.
[12] S. Rahme and N. Meskin, “Adaptive sliding mode observer for sensor fault diagnosis of an industrial gas turbine,” Control Eng. Pract., vol. 38, pp. 57–74, 2015.
[13] N. Zhou, C. Yang, D. Tucker, P. Pezzini, and A. Traverso, “Transfer function development for control of cathode airflow transients in fuel cell gas turbine hybrid systems,” Int. J. Hydrog. Energy, vol. 40, no. 4, pp. 1967–1979, 2015.
[14] L. C. Saikia and S. K. Sahu, “Automatic generation control of a combined cycle gas turbine plant with classical controllers using firefly algorithm,” Int. J. Electr. Power Energy Syst., vol. 53, pp. 27–33, 2013.
[15] R. K. Sahu, S. Panda, and N. K. Yegireddy, “A novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systems,” J. Process Control, vol. 24, no. 10, pp. 1596–1608, 2014.
[16] A. Rodriguez-Martinez, R. Garduno-Ramirez, and L. G. Vela-Valdes, “PI fuzzy gain-scheduling speed control at startup of a gas-turbine power plant,” Energy Convers. IEEE Trans. On, vol. 26, no. 1, pp. 310–317, 2011.
[17] S. S. Tayarani-Bathaie, Z. S. Vanini, and K. Khorasani, “Dynamic neural network-based fault diagnosis of gas turbine engines,” Neurocomputing, vol. 125, pp. 153–165, 2014.
[18] H. A. Yousef, K. AL-Kharusi, M. H. Albadi, and N. Hosseinzadeh, “Load frequency control of a multi-area power system: An adaptive fuzzy logic approach,” Power Syst. IEEE Trans. On, vol. 29, no. 4, pp. 1822–1830, 2014.
[19]    S. Ammar, R. Jia, and W. Xiao, “Control of Gas Turbine’s speed with a Fuzzy logic controller,” 2015.
[20]    S. M. Abuelenin and R. F. Abdel-Kader, “Closed-Form Mathematical Representations of Interval Type-2 Fuzzy Logic Systems,” ArXiv Prepr. ArXiv170605593, 2017.