Abstract
To help operators in nuclear power plant (NPP) identifying an initiating event and take proper actions to avoid the occurrence of a severe accident, several event detection and identification algorithms are proposed in this study. These algorithms can automatically detect the occurrence of an event and identify its type to enable a safe shutdown of the NPP. By monitoring various sensing variables, an event can be detected when the readings exceed the preset limits. Unlike methods in the literature, a statistical approach is applied to set the desired thresholds objectively. After detecting an event, features that are discriminant will be extracted to compare against those of all the events stored in the database to determine its type. In the existing approaches, the features are extracted with emphasis on their temporal aspects, ignoring the spatial information. Thus, this study would focus on the application of the spatial information in event identification. Moreover, to increase the success rate of event identification, only those features that are most discriminant will be retained for identification. This is achieved through ranking the features according to their differentiating capabilities. Lastly, since events belonging to unenrolled classes may happen in practice, an approach to isolate them before identification is required. This is also considered in this study. All the proposed algorithms will be evaluated using data generated by the Maanshan NPP simulator, Modular Accident Analysis Program Version 5 (MAAP5). Eleven event categories having 135 initiating events are included in demonstrating the efficacy and robustness of the proposed algorithms.