a b s t r a c tIf a transient occurs in a nuclear power plant (NPP), operators will try to protect the NPP by estimating the kind of abnormality and mitigating it based on recommended procedures.Similarly, operators take actions based on severe accident management guidelines when there is the possibility of a severe accident occurrence in an NPP. In any such situation, information about the occurrence time of severe accident-related events can be very important to operators to set up severe accident management strategies. Therefore, support systems that can quickly provide this kind of information will be very useful when operators try to manage severe accidents. In this research, the occurrence times of several events that could happen during a severe accident were predicted using support vector machines with short time variations of plant status variables inputs. For the preliminary step, the break location and size of a loss of coolant accident (LOCA) were identified.Training and testing data sets were obtained using the MAAP5 code. The results show that the proposed algorithm can correctly classify the break location of the LOCA and can estimate the break size of the LOCA very accurately. In addition, the occurrence times of severe accident major events were predicted under various severe accident paths, with reasonable error. With these results, it is expected that it will be possible to apply the proposed algorithm to real NPPs because the algorithm uses only the early phase data after the reactor SCRAM, which can be obtained accurately for accident simulations.

As a means of effectively managing severe accidents at nuclear power plants, it is important to identify and diagnose accident initiating events within a short time interval after the accidents by observing the major measured signals. The main objective of this study was to diagnose loss of coolant accidents (LOCAs) using artificial intelligence techniques, such as SVC (support vector classification) and GMDH (group method of data handling). In this study, the methodologies of SVC and GMDH models were utilized to discover the break location and estimate the break size of the LOCA, respectively. The 300 accident simulation data (based on MAAP4) were used to develop the SVC and GMDH models, and the 33 test data sets were used to independently confirm whether or not the SVC and GMDH models work well. The measured signals from the reactor coolant system, steam generators, and containment at a nuclear power plant were used as inputs to the models, and the 60 sec time-integrated values of the input signals were used as inputs into the SVC and GMDH models. The simulation results confirmed that the proposed SVC model can identify the break location and the proposed GMDH models can estimate the break size accurately. In addition, even if the measurement errors exist and safety systems actuate, the proposed SVC and GMDH models can discover the break locations without a misclassification and accurately estimate the break size.

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