Issues in Model Reduction of Power Grids

Abstract

Power grid analysis has recently risen to prominence due to the widespread use of lower supply voltages by power-conscious designs. Low supply voltages imply smaller noise margins and make the voltage drop across the power grid very critical since it can lead to overall slower circuits, signal integrity issues and ultimately to circuit malfunction. Verifying proper behavior of a power grid is a difficult task due to the sheer size of such networks. The usual solution to this problem is to apply reduced-order modeling techniques to generate a smaller macromodel. These techniques are typically based on projections to subspaces whose dimension is determined by the input space. Unfortunately power grids are characterized by a massive number of network ports, which limits the amount of compression achievable. Recently, new algorithms have been proposed for solving this problem which may provide efficient alternatives. In this paper we discuss the main issues related to model reduction of power grid networks and compare several methods for such reduction, providing some insight into the problem and how it can be tackled.

Efficient description of RLC-macromodels with a large number of independent sources for model order reduction

“…With this type of projection matrices the order of the reduced model is proportional to the number of matched moments multiplied with the number of ports and the number of expansion points. For EMC macromodels with a large number of ports, this represents a strong limitation for the possible order reduction [12].…”

“…A high number of independent sources is a limitation for the order reduction algorithms [11], [12] and therewith limits the speed-up of simulations. In this paper an efficient description of the electrical network of models with a large number of independent sources is introduced, which enables a higher order reduction than the standard approach.…”