Pattern Matching Framework to Estimate the Urgency of Off-Normal Situations in NPPs

According to power plant operators, it was said that they could quite well recognize off-normal situations from an incipient stage and also anticipate the possibility of upcoming trips in case of skilled operators, even though it is difficult to clarify the cause of the off-normal situation. From the interview, we could assure the feasibility of two assumptions for the diagnosis of off-normal conditions: One is that we can predict whether an accidental shutdown happens or not if we observe the early stage when an off-normal starts to grow. The other is the observation at the early stage can provide the remaining time to a trip as well as the cause of such an off-normal situation. For this purpose, the development of on-line monitoring systems using various data processing techniques in nuclear power plants (NPPs) has been the subject of increasing attention and becomes important contributor to improve performance and economics. Many of studies have suggested the diagnostic methodologies. One of representative methods was to use the distance discrimination as a similarity measure, for example, such as the Euclidean distance. A variety of artificial intelligence techniques such as a neural network have been developed as well. In addition, some of these methodologies were to reduce the data dimensions for more effectively work. While sharing the same motivation with the previous achievements, this study proposed non-parametric pattern matching techniques to reduce the uncertainty in pursuance of selection of models and modeling processes. This could be characterized by the following two aspects: First, for overcoming considering only a few typical scenarios in the most of the studies, this study is getting the entire sets of off-normal situations which are anticipated in NPPs, which are created by a full-scope simulator. Second, many of the existing researches adopted the process of forming a diagnosis model which is so-called a training technique or a parametric approach. This is the process of fitting the collected data to a pre-set framework. In this study, we proposed the non-parametric approach based pattern matching technique to reduce the uncertainty arising during the selection of models and modeling processes. Preserving the data as collected from off-normal situations, the snapshot data captured in a certain size moving window continues to perform pattern matching with collected data and to determine the most similar case as an off-normal situation. From the database, we are able to provide the remaining time to a trip, in other words, the urgency of off-normal situation