Identification of LOCA and Estimation of Its Break Size by Multiconnected Support Vector Machines

Abstract

Nuclear power plants (NPPs) are composed of very large complex systems. During transient occurrences in NPPs, operators determine the transients of the NPP through information acquired from various measuring instruments. A support vector machine (SVM) based on serial and parallel connections, termed as a multiconnected SVM, is introduced in this paper. The loss of coolant accidents (LOCAs) was identified and their break sizes are estimated using the multiconnected SVM model. The optimal parameter values of the multiconnected SVM models are obtained using a genetic algorithm. In this paper, the modular accident analysis program code was used to simulate the severe accidents occurring due to a variety of design basis accidents. The proposed algorithm uses the short time-integrated simulated sensor signals just after the reactor trip. The results show that the multiconnected SVM model can identify LOCAs and estimate their break sizes accurately. It is expected that the LOCA identification and the accurate estimation of the break size are useful for NPP operators when they try to manage severe accidents.