Identification of reduced order average linear models from nonlinear dynamic simulations

This paper presents a general methodology for the identification of Average Linear Low Order Models (ALLOM) from data collected from detailed nonlinear models; While there are many methods available in the literature for identifying linear models, these methods tend to produce inaccurate and iii-conditioned models when used on non-linear data sets. The method in this paper differs from traditional linearization methods in that it better approximates the dynamic characterisitcs over a wider area around the reference steady state. As it will be argued in subsequent papers, it also provides a better basis for the development of Nonlinear Low Order Models (NLLOM).