Development of a New Support System for Identification/Forecasting of NPPs Transients

This paper introduces the support system for nuclear power plants (NPPs) operators. Transient is identified by a supervised classifier combining auto-regressive integrated moving average (ARIMA) model and error back propagation (EBP) algorithm. Transductive support vector machine (TSVM) as a semi-supervised learning (SSL) is used to cluster the type of unknown transient. To forecast future states of NPPs, a hybrid network combining ARIMA model and EBP learning algorithm is developed. Bushehr nuclear power plant (BNPP) transients are probed to analyze the ability of the proposed system. Noticeable advantages are: clustering of unknown transients, sole dependency of identifier on sign of output signal, forecasting any quantifiable parameter without necessity to know its correlation with other, possibility for prediction of parameters in long temporal dependencies. Finally, modular decomposition of the developed support system is presented. The developed system will afterwards be completed by necessary interfaces to be installed on the BNPP full scale simulator to verify its applicability and performance.