Date of Award
Master of Engineering Science
Electrical and Computer Engineering
Dr. Jin Jiang
In this thesis, the water level control problem of U-Tube Steam Generators (UTSG) of Nuclear Power Plants (NPP) is investigated. The water level of a UTSG must be regulated within an admissible range to assure safe and economic operation. Additionally, a high dynamic performance is desired when adjusting the level within the admissible range, because poor performance could eventually shorten the designed life of various instruments of the SG. Poor performance can also degrade the quality of the generated power. Difficulty in controlling the water level is mainly due to the highly nonlinear and inverse dynamics of the UTSG caused by a non-minimum phase phenomenon known as the swell and shrink effect.
This thesis focuses on the synthesis of a set-point function to improve the performance of the UTSG level control system under the presence of NPP power changes. The proposed set-point function is based on the concept of inverse control theory. Future information on the change in demanded power is used by the proposed control scheme to apply the set-point function pre-emptively. This pre-emptive control action allows the control system to prepare itself for the upcoming change in power. This preparation improves the performance of the control system considerably.
Using the Irving UTSG model, simulation results within MATLAB/SIMULINK show that the proposed control scheme is capable of regulating the level within the admissible range effectively. Regulation and level adjustment performance, herein, is measured in terms of the percentage overshoot and percentage undershoot of the level response. When compared to the widely used swell-based set-point function, the proposed control scheme can reduce the percentage overshoot and percentage undershoot by as much as 35.4% and 69.7%, respectively.
Akkawi, Mahmood, "An Inverse Control-Based Set-point Function for Level Control in a U-Tube Steam Generator for Nuclear Power Plants" (2011). Digitized Theses. 3260.