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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 | ||
| مقاله 3، دوره 46، شماره 2، بهمن 2014، صفحه 23-30 اصل مقاله (756.46 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2014.574 | ||
| نویسندگان | ||
| A. Fakharian* 1؛ R. Mosaferin2؛ M. B. Menhaj3 | ||
| 1Assistant Professor, Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran | ||
| 2Department of Mechatronics Engineering, South Branch, Islamic Azad University, Tehran, Iran | ||
| 3Professor, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| چکیده | ||
| 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 | ||
| کلیدواژه‌ها | ||
| Recurrent fuzzy-neural network (RFNN)؛ Gas turbine؛ Neural Network؛ Direct Control Model | ||
| عنوان مقاله [English] | ||
| A New Recurrent Fuzzy Neural Network Controller Design for Speed and Exhaust Temperature of a Gas Turbine Power Plant | ||
| نویسندگان [English] | ||
| A. Fakharian1؛ R. Mosaferin2؛ M. B. Menhaj3 | ||
| 1Assistant Professor, Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran | ||
| 2Department of Mechatronics Engineering, South Branch, Islamic Azad University, Tehran, Iran | ||
| 3Professor, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| چکیده [English] | ||
| 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 | ||
| کلیدواژه‌ها [English] | ||
| Recurrent fuzzy-neural network (RFNN), Gas turbine, Neural Network, Direct Control Model | ||
| مراجع | ||
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[1] A. Kostyuk and V. Frolov, steam and gas turbines 2ndedition, Mire publisher, Moscow,1988, Transl. D. Tavakoli, and S.R. Shamshirgaran, Iran. [2] W. I. Rowen, “Simplified mathematical representation of heavy-duty gas turbine,” ASME, Journal of eng. Gas Turbines and Power, vol. 105, pp. 865-869, 1983. [3] M. R. Bank Tavakoli, B. Vahidi, and W. Gawlik, “An Educational Guide to Extract the Parameters of Heavy Duty Gas Turbines Model in Dynamic Studies Based on Operational Data,” IEEE Trans. Power Syst., vol. 24, no. 3, pp. 1366–1374, Aug. 2009. [4] Working Group, on Prime Mover and Energy Supply Models, “Dynamic models for fossil fueled steam units in power system studies,” IEEE Trans. Power Syst., vol. 6, no. 2, pp. 753–761, May 1991. [5] Working Group, on Prime Mover and Energy Supply Models, “Hydraulic turbine and turbine control models for system dynamic studies,” IEEE Trans. Power Syst., vol. 7, no. 1, pp. 167–179, Feb. 1992. [6] IEEE Working Group Report, “Dynamic models for combined cycle plants in power system studies,” IEEE Trans. Power Syst., vol. 9, no. 3, pp. 1698–1707, Aug. 1994. [7] A. R. Martinez, R. G. Ramirez and L. G. Vela-Valdes, “PI Fuzzy Gain Scheduling Speed Control at Startup of a Gas-Turbine Power Plant,” IEEE Trans. Energy Conversion, vol. 26, no. 1, March 2011. [8] D. H. Kim, “Neuro-fuzzy tuning of PID controller for control of actual gas turbine power,” IEEE inter. conf. computational intelligence for measurements and applications, pp. 192–197, July 2004. [9] S. Balamurugan, R. J. Xavier, and A. E. Jeyakumar, “Control of Heavy-duty Gas Turbine Plants for Parallel Operation Using Soft Computing Techniques,” Taylor and Francis, Electric Power Components and Systems, vol. 37, no. 11, pp. 1275-87, Oct. 2009. [10] S. M. Camporeale, B. Fortunato and A. Dumas, “Non-linear simulation model and multivariable control of a regenerative single shaft gas turbine,” IEEE Inter. Conf., pp. 721-723, Oct. 1997. [11] A. Marzoughi, H. Selamat, M. F. Rahmat and H. A. Rahim, “Optimized proportional integral derivative (PID) controller for the exhaust temperature control of a gas turbine system using particle swarm optimization,” Inter. Journal of the Physical Sciences, vol. 7, no. 5, pp. 720-729, Jan. 2012. [12] J. W. Kim and S. W. Kim, “Design of Incremental Fuzzy PI Controllers for A Gas-Turbine Plant,” IEEE/ASME Trans. Mechatronics, vol. 8, no. 3, pp. 410-414, September 2003. [13] H. Ghorbani, A. Ghaffari, and M. Rahnama, “Constrained Model Predictive Control Implementation for a Heavy-Duty Gas Turbine Power Plant,” WSEAS Trans. system and control, vol. 3, no. 6, pp. 507-516, June 2008. [14] E. Najimi, and M. H. Ramezani, “Robust control of speed and temperature in a power plant gas turbine,” Elsevier, ISA Trans., vol. 51, no. 2, March 2012. [15] W. Gua, Z. Wub, R. Boc, W. Liua, G. Zhoua, W. Chena and Z. Wua, “Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review,” International Journal of Electrical Power and Energy Systems, vol. 54, pp. 26-37, 2014. [16] Shuvom, M. and Haq, M., "Development and Analysis of Adaptive Neural Network Control for a Cybernetic Intelligent ‘iGDI’ Engine," SAE Technical Paper 2015-01-0157, 2015. [17] C. H. Lee and C. C. Teng, “Identification and control of dynamic systems using recurrent fuzzy neural networks,” IEEE Trans. Fuzzy Syst., vol. 8, no. 4, pp. 349 -366, August 2000. [18] F. J. Lin, R. J. Wai, K. K. Shyu, and T. M. Liu, “Recurrent fuzzy neural network control for piezoelectric ceramic linear ultrasonic motor drive,” IEEE Trans. Ultrason., Ferroelect., Freq. Contr., vol. 48, pp. 900 -913, July 2001. [19] A. R. Martinez, R. G. Ramirez and L. G. Vela-Valdes, “PI Fuzzy Gain Scheduling Speed Control at Startup of a Gas-Turbine Power Plant,” IEEE Trans. Energy Conversion, vol. 26, no. 1, March 2011. [20] K. P. Venugopal, R. Sudhakar and A. S. Pandya, “An improved scheme for direct adaptive control of dynamical systems using back propagation neural networks,” J. Circuits, Syst. Signal Processing, vol. 14, no. 2, pp. 213 -236, 1995. | ||
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