Design and application of supervisory control based on neural network PID controllers for pressurizer system

Abstract

The pressurizer system (PRZ) plays a vital role in the operation of pressurized water reactors (PWRs). The PRZ is inherently a non-linear, time-variant system, and hence the PRZ modeling with transfer function or linear time-invariant state-space forms is not accurate enough. In this paper, the thermal-hydraulic behavior of the PRZ is modeled by RELAP5 thermal-hydraulic code that is one of the best modeling codes offered for the PRZ system. Although the RELAP5 best-estimate code is appropriate for PRZ modeling, the RELAP5 is not capable to implement the advanced controllers. As first-time, to eliminate this issue, the RELAP5 code and MATLAB software are coupled to use the capabilities of MATLAB (the ability to implement advanced controllers) and RELAP5 (the best model for the PRZ system) simultaneously. Accordingly, this coupling provides a new platform for designing and implementing various intelligent and advanced controllers for PRZ pressure and level in RELAP5 code. Likewise, after providing the platform for implementing intelligent controllers, a novel supervisory control based on neural network PID (NN-PID) controllers is designed for the PRZ pressure and level control. Also, the PRZ system is modeled as a MIMO system with consideration of the PRZ pressure and level interactions. Furthermore, as a case study, the advanced designed control system is applied to adjust the pressure and level of PRZ in a VVER-1000 type nuclear power plant. The study results show that this newly designed control system is able to control the PRZ pressure and level efficiently and effectively in several conditions. Therefore, the proposed methodology as a novel design of PRZ control can be applied and developed in new generations of nuclear power plants and pressurized test loops.