Fakharian, A., Mosaferin, R., Menhaj, M. (2014). A New Recurrent Fuzzy Neural Network Controller Design for Speed and Exhaust Temperature of a Gas Turbine Power Plant. AUT Journal of Modeling and Simulation, 46(2), 23-30. doi: 10.22060/miscj.2014.574

A. Fakharian; R. Mosaferin; M. B. Menhaj. "A New Recurrent Fuzzy Neural Network Controller Design for Speed and Exhaust Temperature of a Gas Turbine Power Plant". AUT Journal of Modeling and Simulation, 46, 2, 2014, 23-30. doi: 10.22060/miscj.2014.574

Fakharian, A., Mosaferin, R., Menhaj, M. (2014). 'A New Recurrent Fuzzy Neural Network Controller Design for Speed and Exhaust Temperature of a Gas Turbine Power Plant', AUT Journal of Modeling and Simulation, 46(2), pp. 23-30. doi: 10.22060/miscj.2014.574

Fakharian, A., Mosaferin, R., Menhaj, M. A New Recurrent Fuzzy Neural Network Controller Design for Speed and Exhaust Temperature of a Gas Turbine Power Plant. AUT Journal of Modeling and Simulation, 2014; 46(2): 23-30. doi: 10.22060/miscj.2014.574

A New Recurrent Fuzzy Neural Network Controller Design for Speed and Exhaust Temperature of a Gas Turbine Power Plant

^{1}Assistant Professor, Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

^{2}Department of Mechatronics Engineering, South Branch, Islamic Azad University, Tehran, Iran

^{3}Professor, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

In this paper, a recurrent fuzzy-neural network (RFNN) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. Since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command signal and inlet guide vane position. Also practical limitations are applied to system inputs. In addition, demand power and ambient temperature are considered as disturbance. Simulation results show the effectiveness of proposed controller in comparison with other conventional methods such as Model Predictive Control (MPC) and H_{∞} control in a same operating condition

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