Advanced Feedwater Control for Next Generation Nuclear Power Systems

Abstract

In current generation Pressurized Water Reactors (PWRs), the control of Steam Generator level experiences challenges over the full range of plant operating conditions. These challenges can be particularly troublesome in the low power range where the feedwater is highly subcooled and minor changes in the feed flow may cause oscillations in the SG level, potentially leading to reactor trip.

Substantial attention has been given to feedwater control systems with recognition of the difficulty of the full range feedwater control problem due to steam generator level shrink-swell phenomena, changes in valve and flow path characteristics, and other nonlinear phenomena over the full range of operating conditions[1]. The IRIS reactor concept adds additional challenges to the feedwater control problem as a result of a steam generator design where neither level or steam generator mass inventory can be measured directly[2].

Neural networks have demonstrated capabilities to capture a wide range of dynamic signal transformation and non-linear problems[3-5]. In this project a detailed engineering simulation of plant response is used to develop and test neural control methods for the IRIS full range feedwater control problem. The established neural feed controller has demonstrated the capability to improve the performance of SG level or mass control under transient conditions and over a wide range of reactor power including abnormal conditions.