Hierarchical power delivery network analysis via bipartite markov chains

Pei Yu Huang*, Yu-Min Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper proposes a Markov-chain-based hierarchical method to efficiently analyze the power delivery network. After the network is partitioned into several sub-networks, each sub-network is transformed into a local Markov chain. Then, the connectivity between all the sub-networks is modeled as a global Markov chain by using a novel technique called parallel-street-walk. Finally, these local and global Markov chains are integrated in order to build a hierarchical bipartite Markov chain engine to analyze the power delivery network. The experimental results not only demonstrate the accuracy of our proposed method when compared with a very accurate time-domain solver, INDUCTWISE [2], but also show a significant runtime improvement- over 200 times faster than the INDUCTWISE and over 10 times faster than the IEKS method [3] - and low memory consumption.

Original languageEnglish
Pages (from-to)121-132
Number of pages12
JournalInternational Journal of Electrical Engineering
Issue number2
StatePublished - 1 Apr 2009


  • Hierarchical analysis
  • Markov chain
  • Power distribution network

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