Model Reduction Methods for Linear Network Models of Distributed Systems with Sources

Abstract

AbstractDistributed systems with distributed sources are modeled as large electrical networks with linear RLC-elements, independent sources and pins for the connection with other network models. Often these networks are too large to be simulated efficiently. Model reduction can be used to reduce these networks while approximating the behavior at the pins for the connection with other nonlinear and linear network models. In the standard model reduction the independent sources are extracted and connected by ports with the RLC-part of the network which is to be reduced. This extraction only enables a weak reduction for networks with a large number of independent sources, as the number of ports is very high. In this article an efficient reduction of networks with a large number of sources is proposed by taking the waveforms of the sources into account. The method is based on the reduction of the dimension of the function space of the waveforms with the help of function approximation. The basis functions of the function approximation are used in the network, which results a lower number of independent sources. Extracting this lower number of independent sources and reducing the network enables a higher model reduction with state of the art techniques. With the proposed method a smaller size and higher accuracy of the reduced model can be achieved. The validity and efficiency of the proposed method is shown by reducing example models.KeywordsReduced order modelingLinear RLC networks with sourcesGeneralized state space systemsTerminalsImpedanceModified model analysis