Nuclear power plant monitoring using data-driven modeling techniques

Abstract

This paper presents a monitoring algorithm using the Group Method for Data Handling (GMDH) that creates algebraic models for system characterization. The monitoring system was applied to the IEA-R1 experimental reactor at IPEN, Brazil. The IEA-RI is a 5 MW pool-type research reactor that uses light water as coolant and moderator, and graphite as reflector. The GMDH provides a general framework for characterizing the relationship among a set of state variables of a process and is used for generating estimates of critical variables in an optimal data-driven model form. The monitoring system developed in this work was used to predict the IEA-R1 reactor hall dose rate, based on nuclear power and rod position variables. The results obtained using the GMDH models agreed very well with the dose rates measurements.