Prediction of golden time using SVR for recovering SIS under severe accidents

Abstract Nuclear power plants (NPPs) are designed in consideration of design basis accidents (DBAs). However, if the safety injection system (SIS) is not working properly in a loss-of-coolant-accident (LOCA) situation, it can induce a severe accident that exceeds DBAs. Therefore, it is important to properly actuate the SIS before a DBA becomes a severe accident. If the SIS is not working in time, the reactor core may be uncovered and the reactor vessel (RV) may be damaged. In this paper, we defined the golden time as the available time from an initial SIS malfunction for actuating the SIS to prevent reactor core uncovery and RV failure. A support vector regression (SVR) model was applied to predict the golden time. The input variables and parameters of the SVR model were selected and optimized by using a genetic algorithm. The data set of severe accident scenarios was obtained by using the Modular Accident Analysis Program (MAAP) code. An optimized power reactor (OPR1000) was used for the simulations. It was shown that that the proposed SVR model could predict the golden time accurately.

[1]  Man Gyun Na,et al.  Detection and Diagnostics of Loss of Coolant Accidents Using Support Vector Machines , 2008, IEEE Transactions on Nuclear Science.

[2]  Jong-Ho Park,et al.  A Study on Severe Accident Management Scheme using LOCA Sequence Database System , 2014 .

[3]  Maryam Abbasi,et al.  Uncertainty analysis of support vector machine for online prediction of five-day biochemical oxygen demand , 2015 .

[4]  Man Gyun Na,et al.  Inferential Sensing and Monitoring for Feedwater Flowrate in Pressurized Water Reactors , 2006, IEEE Transactions on Nuclear Science.

[5]  Xiaobing Kong,et al.  Wind speed prediction using reduced support vector machines with feature selection , 2015, Neurocomputing.

[6]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[7]  Dimitri P. Bertsekas,et al.  Constrained Optimization and Lagrange Multiplier Methods , 1982 .

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  Man Gyun Na,et al.  PREDICTION OF THE REACTOR VESSEL WATER LEVEL USING FUZZY NEURAL NETWORKS IN SEVERE ACCIDENT CIRCUMSTANCES OF NPPS , 2014 .

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  Man Gyun Na,et al.  PREDICTION OF HYDROGEN CONCENTRATION IN CONTAINMENT DURING SEVERE ACCIDENTS USING FUZZY NEURAL NETWORK , 2015 .

[12]  Man Gyun Na,et al.  Smart Sensing of the RPV Water Level in NPP Severe Accidents Using a GMDH Algorithm , 2014, IEEE Transactions on Nuclear Science.

[13]  Belle R. Upadhyaya,et al.  Sensor monitoring using a fuzzy neural network with an automatic structure constructor , 2003 .