Browse > Article
http://dx.doi.org/10.5762/KAIS.2012.13.3.1255

Design of a Neural Network PI Controller for F/M of Heavy Water Reactor Actuator Pressure  

Lim, Dae-Yeong (Electronic engineering, Chonbuk National University)
Lee, Chang-Goo (Electronic engineering, Chonbuk National University)
Kim, Young-Baik (Electronic engineering, Chonbuk National University)
Kim, Young-Chul (Mechanical engineering, Kunsan National University)
Chong, Kil-To (Electronic engineering, Chonbuk National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.13, no.3, 2012 , pp. 1255-1262 More about this Journal
Abstract
Look into the nuclear power plant of Wolsong currently, it is controlled in order to required operating pressure with PI controller. PI controller has a simple structure and satisfy design requirements to gain setting. However, It is difficult to control without changing the gain from produce changes in parameters such as loss of the valves and the pipes. To solve these problems, the dynamic change of the PI controller gain, or to compensate for the PI controller output is desirable to configure the controller. The aim of this research and development in the parameter variations can be controlled to a stable controller design which is reduced an error and a vibration. Proposed PI/NN control techniques is the PI controller and the neural network controller that combines a parallel and the neural network controller part is compensated output of the controller for changes in the parameters were designed to be robust. To directly evaluate the controller performance can be difficult to test in real processes to reflect the characteristics of the process. Therefore, we develope the simulator model using the real process data and simulation results when compared with the simulated process characteristics that showed changes in the parameters. As a result the PI/NN controller error and was confirmed to reduce vibrations.
Keywords
Heavy Water Reactors; Pressure Control; NNC; MATLAB SIMUINK; Power Plant;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 J. A. Rovnak, Dr. R. Corlis, "DYNAMIC MATRIX BASED CONTROL OF FOSSIL POWER PLANTS", IEEE, Vol. 6, No. 2, 1991.
2 KiYong Oh, Hoyol Kim, "Unit Response Optiizer mode Design of Ultra Super Critical Coal-Fired Power Plant based on Fuzzy logic & Model Predictive Controller", KIEE. Vol. 57, No. 12, pp. 2285-2290, 2008.
3 Wee-Jae Shin, "Fuzzy Scheduling for the PID Gain Tuning", KIIS, Vol.15, No,1, pp. 120-125, 2005.
4 S. Lu, B.W.Hogg, "Dynamic nonlinear modelling of power plant by physical principles and neural networks", Electrical Power and Energy System, No. 22, pp. 67-78, 2000.   DOI   ScienceOn
5 Serhat Seker, "Elman's recurrent neural network applications to condition monitoring in nuclear power plant and rotating machinery", EAAI, pp. 647-656, 2003.
6 Yang Oh, "The Speed Control of Induction Motor using PD Controller and Neural Networks", IEEK, Vol. 39, No. 2, pp. 73-81, 2002.
7 Hyunseob Cho, "Study on the Speed Control of Induction Motor using a PID Controller and Neural Network Controller", KAIS, Vol. 10, No. 8, pp. 1993-1997, 2009.
8 Lide, D.R. "Handbook of Chemistry and Physics, 73rd Edition", CRC, Boca Raton, 1992, Chapter 6, pp. 13.
9 Jacek M. Zirada. "Artificial Neural Systems", pp. 185-206.
10 Suyeong Ha, "Domestic Nuclear Power Plant Operating Status", Journal of mechanical engineers, Vol. 48, No. 4, pp. 44-48, 2008.
11 KOREA HYDRO & NUCLEAR POWER CO.,LTD, "Domestic Nuclear Status", 2011.
12 G. PRASAD, E. SWIDENBANK, and B. W. Hogg, "A Local Model Networks Based Multivariable Long-Range Predictive Control Strategy for Thermal Power Plants", Elsevier Science, Vol. 34, No. 10, pp. 1185-1204, 1998.