Data-Driven Model Reduction by Moment Matching for Linear and Nonlinear Parametric Systems

TL;DR

This paper introduces new data-driven and model-based methods for parametric model reduction via moment matching, applicable to both linear and nonlinear systems, ensuring key properties are preserved.

Contribution

It defines the parametric moment and proposes novel approximation techniques for linear and nonlinear systems, enabling effective reduced-order modeling.

Findings

Methods successfully applied to a wind farm nonlinear model

Comparison shows advantages of different approximation techniques

Reduced models preserve stability and dissipativity

Abstract

Theory and methods to obtain parametric reduced-order models by moment matching are presented. The definition of the parametric moment is introduced, and methods (model-based and data-driven) for the approximation of the parametric moment of linear and nonlinear parametric systems are proposed. These approximations are exploited to construct families of parametric reduced-order models that match the approximate parametric moment of the system to be reduced and preserve key system properties such as asymptotic stability and dissipativity. The use of the model reduction methods is illustrated by means of a parametric benchmark model for the linear case and a large-scale wind farm model for the nonlinear case. In the illustration, a comparison of the proposed approximation methods is drawn and their advantages/disadvantages are discussed.