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Estimation of LOCA Break Size Using Cascaded Fuzzy Neural Networks
최건필,유쾌환,백주현,나만균 한국원자력학회 2017 Nuclear Engineering and Technology Vol.49 No.3
Operators of nuclear power plants may not be equipped with sufficient information during aloss-of-coolant accident (LOCA), which can be fatal, or they may not have sufficient time toanalyze the information they do have, even if this information is adequate. It is not easy topredict the progression of LOCAs in nuclear power plants. Therefore, accurate information ontheLOCAbreak positionandsize should be provided to efficientlymanage the accident. In thispaper, the LOCA break size is predicted using a cascaded fuzzy neural network (CFNN) model. The input data of theCFNNmodel are the time-integrated values of each measurement signalfor an initial short-time interval after a reactor scram. The training of the CFNN model isaccomplished by a hybrid method combined with a genetic algorithm and a least squaresmethod. As a result, LOCA break size is estimated exactly by the proposed CFNN model.
원자력발전소 기동 및 정지 운전을 위한 순환 신경망 기반 인공지능 프레임워크 개발
구서룡,김현민,최건필,김정택 제어·로봇·시스템학회 2019 제어·로봇·시스템학회 논문지 Vol.25 No.9
In order to reduce operator workload from startup and shutdown operations for existing Nuclear Power Plants (NPPs), it is necessary to develop an automation system based on deep learning, the leading approach in current Artificial Intelligence (AI) technology. From existing research, it is challenging to develop an automation system using conventional machine learning for startup and shutdown operation since the automation system needs to be able to handle many instances of both monitoring and control variables in NPPs. Deep learning is able to simulate a variety of operating actions based on the experience of each operator. In this study, an AI framework for an automation system for startup operation in NPPs has been developed using a Recurrent Neural Network (RNN), which is a robust deep learning method for time series analysis. A feasibility study for an AI framework for the automation system is conducted using a Compact Nuclear Simulator (CNS) based on Westinghouse three-loop NPPs. The target scenario for the feasibility study is operation under bubble creation conditions in a pressurizer under startup.