Günyaz ABLAY

A robust estimator-based optimal algebraic approach to steam generator feedwater control system

Feedwater control systems are used to maintain the steam generator water level within prescribed narrow limits and to provide constant supply of steam during power demand variations. Current feedwater control systems are often found to be unsatisfactory during startup and low power operations. A robust nonlinear estimator-based optimal algebraic control is developed for feedwater control systems to solve the water level tracking problem during power demand variations. It is shown that the proposed control provides an optimal and robust water level tracking with a single automatic controller over the complete range of power operations in the presence of plant uncertainties and noisy measurements.

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[1] Irving M, Miossec C, Tassart J. Towards efficient full automatic operation of the PWR steam generator with water level adaptive control. In: Proceedings of the 2nd International Conference on Boiler Dynamics and Control in Nuclear Power Stations; 2325 October 1979; Bournemouth, UK. pp. 309329.
[2] Menon SK, Parlos AG. Gain-scheduled nonlinear control of U-tube steam generator water level. Nucl Sci Eng 1992; 111: 294308.
[3] Tong LS. Principles of Design Improvement for Light Water Reactors. Berlin, Germany: Springer, 1988.
[4] Suh GW, No HC. Dynamic modeling and optimum level controller design for steam generators in pressurized water reactors. Nucl Sci Eng 1985; 90: 236247.
[5] Choi JI, Meyer E, Lanning DD. Automatic controller for SG water level during low power operation. Nucl Eng Des 1989; 117: 263274.
[6] Dong W, Doster JM, Mayo CW. Steam generator control in nuclear power plants by water mass inventory. Nucl Eng Des 2008; 238: 859871.
[7] Futao Z, Wei D, Yiheng X, Zhiren H. Programmable logic controller applied in steam generators water levels. In: IEEE Industry Applications Conference; 610 October 1996; San Diego, CA, USA. New York, NY, USA: IEEE. pp.15151556.
[8] Liu C, Zhao F, Hu P, Hou S, Li C. P controller with partial feed forward compensation and decoupling control for the steam generator water level. Nucl Eng Des 2010; 240: 181190.
[9] Na MG. Auto-tuned PID controller using a model predictive control method for the steam generator water level. IEEE T Nucl Sci 2001; 48: 16641671.
[10] Zhao F, Ou J, Du W. Simulation modeling of nuclear steam generator water level process - a case study. ISA T 2000; 39: 143151.
[11] Parlos AG, Rais OT. Nonlinear control of U-tube steam generators via Hinf control. Control Eng Pract 2000; 8: 921936.
[12] Sohn JJ, Seong PH. A steam generator model identification and robust H∞ controller design with ν -gap metric for a feedwater control system. Ann Nucl Energy 2010; 37: 180195.
[13] Kim M, Shin M, Chung M. A gain-scheduled L2 control to nuclear steam generator water level. Ann Nucl Energy 1999; 26: 905916.
[14] Hu K, Yuan J. Multi-model predictive control method for nuclear steam generator water level. Energ Convers Manage 2008; 49: 11671174.
[15] Kothare MV, Mettler B, Morari M, Bendotti P. Level control in the steam generator of a nuclear power plant. IEEE T Contr Syst T 2000; 8: 5569.
[16] Lee YJ, Oh SJ, Chun W, Kim NJ. The model predictive controller for the feedwater and level control of a nuclear steam generator. Nucl Eng Technol 2012; 44: 911918.
[17] Na MG, Lee YJ. A receding horizon controller for the steam generator water level. Nucl Technol 2003; 143: 180196.
[18] Dong Z, Huang X, Feng J. Water-level control for the U-tube steam generator of nuclear power plants based on output feedback dissipation. IEEE T Nucl Sci 2009; 56: 16001612.
[19] Basher AMH, March-Leuba J. Development of a robust model-based water level controller for U-tube steam generator. Oak Ridge National Laboratory Report ORNL/TM-2001/166. Oak Ridge, TN, USA: Oak Ridge National Laboratory; 2001.
[20] Na MG, No HC. Design of an adaptive observer-based controller for the water level of steam generators. Nucl Eng Des 1992; 135: 379394.
[21] Marseguerra M, Zio E, Cadini F. Optimized adaptive fuzzy controller of the water level of a pressurized water reactor steam generator. Nucl Sci Eng 2007; 155: 386394.
[22] Munasinghe SR, Kim M, Lee J. Adaptive neurofuzzy controller to regulate UTSG water level in nuclear power plants. IEEE T Nucl Sci 2005; 52: 421429.
[23] Ahmad Z. Data-driven controller of nuclear steam generators by set membership approximation. Ann Nucl Energy 2010; 37: 512521.
[24] Wei L, Fang F, Shi Y. Adaptive backstepping-based composite nonlinear feedback water level control for the nuclear U-tube steam generator. IEEE T Contr Syst T 2014; 22: 369377.
[25] Ansarifar GR, Talebi HA, Davilu H. Adaptive estimator-based dynamic sliding mode control for the water level of nuclear steam generators. Prog Nucl Energ 2012; 56: 6170.
[26] Moradi H, Saffar-Avval M, Bakhtiari-Nejad F. Sliding mode control of drum water level in an industrial boiler unit with time varying parameters: A comparison with H∞-robust control approach. J Process Contr 2012; 22:18441855.
[27] Ablay G. Sliding mode approaches for robust control, state estimation, secure communication, and fault diagnosis in nuclear systems. PhD, Ohio State University, Columbus, OH, USA, 2012.
[28] Guarro S, Yau M, Motamed M. Development of tools for safety analysis of control software in advanced reactors. US Nuclear Regulatory Commission, NUREG/CR-6465; 1996.
[29] Na MG, No HC. Quantitative evaluation of swelling or shrinking level contributions in steam generators using spectrum analysis. Ann Nucl Energy 1993; 20: 659666.
[30] Butterfield MH. Dynamics and Control in Nuclear Power Stations. London, UK: Thomas Telford, 1992.
[31] Tan W. Water level control for a nuclear steam generator. Nucl Eng Des 2011; 241: 18731880.
[32] Boiko I, Sayedain S. Analysis of dynamic nonlinearity of flow control loop through modified relay test probing. Int J Control 2010; 83: 25802587.
[33] Leimkuhler B, Sherikar SV. Getting optimum performance through feedwater control modifications. In: Sixth EPRI Valve Technology Symposium; 1997; Portland, ME, USA.
[34] Aldemir T, Guarro S, Kirschenbaum J, Mandelli D, Mangan LA, Bucci P, Yao M, Johnson B, Elks C, Ekici E et al. A benchmark implementation of two dynamic methodologies for the reliability modeling of digital instrumentation and control systems. US Nuclear Regulatory Commission, NUREG/CR-6985; 2009.
[35] Chu TL, Martinez-Guridi G, Yue M, Lehner J, Samanta P, Kuritzky A. Traditional probabilistic risk assessment methods for digital systems. US Nuclear Regulatory Commission, NUREG/CR-6962; 2008.
[36] Doran PK, Williams W, Bonner JM. Improving secondary plant performance and reliability using digital control systems at Calvert Cliffs nuclear power plant (CCNPP), units 1 and 2. In: IEEE Conference on Nuclear Science Symposium and Medical Imaging; 1995; Norfolk, VA, USA. New York, NY, USA: IEEE; 1995.
[37] Chen CT. Linear System Theory and Design. New York, NY, USA: Oxford University Press, 1999.
[38] Kucera V. Polynomial control: past, present, and future. Int J Robust Nonlinear Control 2007;17: 682705.
[39] Manabe S. Unified interpretation of classical, optimal, and Hinf control. Journal of SICE 1991; 30: 941946.
[40] Manabe S, Kim YC. Recent development of coefficient diagram method. In: 3rd Asian Control Conference; 2000; Shanghai, China. pp. 20552060.
[41] Kim YC, Keel LH, Bhattachayya SP. Transient response control via characteristic ratio assignment. IEEE T Automat Contr 2003; 48: 22382244.
[42] Lipatov AV, Sokolov NI. Some sufficient conditions for stability and instability of continuous linear stationary systems. Automat Rem Contr + 1979; 39: 12851291.
[43] M´arquez H. Nonlinear Control Systems: Analysis and Design. Hoboken, NJ, USA: Wiley, 2003.