Determination of Gain Scheduling Parameters for Loss of a Feedwater Pump Transient Mitigation Using Neural Networks

Abstract

Transient mitigation for nuclear power plants is essential for safe operation. The fourth industrial revolution brings with it the potential for data-based predictive maintenance and identifying remaining time of life for degrading components. An improvement to predictive maintenance would be to address continued operation with faulty components between the time of identification and eventual replacement. The ability to perform data analysis and contemporary digital control systems allows for data-driven control system actions. A methodology is developed herein to train a neural network that can map desired system performance and current plant component capability to control system settings. Simulations of plant transients were recorded and used to train a neural network. This neural network was tested with different target performance goals. The results show that the trained neural network recommended settings that affected the control system response so as to meet the target performance goals.