Neural Network Controller for the Pressurized Water Reactor Power Control

Although there have been some severe nuclear accidents such as Three Mile Island (USA), Chernobyl (Ukraine) and Fukushima (Japan), nuclear fission energy is still a source of clean energy that can substitute fossil fuels in a centralized way and in a great amount with commercial availability and economic competitiveness. Since the pressurized water reactor (PWR) is the most widely used nuclear fission reactor, it is safe, stable and efficient operation is meaningful to the current rebirth of the nuclear fission energy industry. Power-level regulation is an important technique which can deeply affect the operation stability and efficiency of PWRs (Pressurized Water Reactors). This paper presents the effect of utilizing the Neural Network controller methodology in the power control model of the PWR. The Neural Network Controller was tested on a PWR model using the Matlab Simulink Interface. Two case studies were performed on the model using both the Neural Network method and the traditional rod speed program for controlling the nuclear power plant variables. The proposed controller presents a higher performance than that of the traditional rod speed program controller.