In modeling of distributed systems with distributed sources large networkswith RLC-elements and independent sources arise. This high complexity leadsto a high effort in simulations. Therefore model reduction can be used toreduce these networks, preserving the behavior at the observed nodes in thenetworks. For the reduction of networks with a large number of independentsources only a weak reduction is enabled with standard model reductiontechniques. In this paper an efficient reduction of networks with a largenumber of sources with piece-wise-linear waveforms is presented, using thedecomposition of piece-wise-linear functions. With the proposed method ahigher reduction of the network and/or a higher accuracy can be achievedwith model reduction. The validity and efficiency of the proposed method isshown by reducing a RCI-Grid model.
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