Faulty Signal Restoration Algorithm in the Emergency Situation Using Deep Learning Methods

Abstract

To operate nuclear power plants (NPPs) safely and efficiently, signals from sensors must be valid and accurate. Signals deliver the current situation and status of the system to the operator or systems that use them as inputs. Therefore, faulty signals may degrade the performance of both control systems and operators in the emergency situation, as learned from past accidents at NPPs. Moreover, With the increasing interest in autonomous and automatic controls, the integrity and reliability of input signals becomes important for the successful control. This study proposes an algorithm for the faulty signal restoration under emergency situations using deep convolutional generative adversarial networks (DCGAN) that generates a new data from random noise using two networks (i.e., generator and discriminator). To restore faulty signals, the algorithm receives a faulty signal as an input and generates a normal signal using a pre-trained normal signal distribution. This study also suggests optimization steps to improve the performance of the algorithm. The optimization consists of three steps; 1) selection of optimal inputs, 2) determine of the hyper-parameters for DCGAN. Then, the data for implementation and optimization are collected by using a Compact Nuclear Simulator (CNS) developed by the Korea Atomic Energy Research Institute (KAERI). To reflect the characteristics of actual signals in NPPs, Gaussian noise with a 5% standard deviation is also added to the data.