Model reduction technique applied to a nuclear reactor turbine system

Abstract

A method has been developed for determining a low-order model for a large multivariable system from its measured input-output data. It is based on the use of a matrix pseudo-inverse to estimate the parameters of the model which minimize the sum of the squares of the errors between the responses of the original system and the low-order model at the sampling instants. A recursive algorithm is proposed which makes it useful for on-line applications. The method is used to obtain low-order models for a nuclear reactor turbine system. The outputs of the low-order models and those of the large-scale model (consisting of 11 non-linear differential equations and 36 algebraic equations) are compared for the turbine system.