Power cyber-physical system risk area prediction using dependent markov chain and improved grey wolf optimization

Zhaoyang Qu, Qianhui Xie, Yuqing Liu, Yang Li, Lei Wang, Pengcheng Xu, Yuguang Zhou, Jian Sun, Kai Xue, Mingshi Cui

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Existing power cyber-physical system (CPS) risk prediction results are inaccurate as they fail to reflect the actual physical characteristics of the components and the specific operational status. A new method based on dependent Markov chain for power CPS risk area prediction is proposed in this paper. The load and constraints of the non-uniform power CPS coupling network are first characterized, and can be utilized as a node state judgment standard. Considering the component node isomerism and interdependence between the coupled networks, a power CPS risk regional prediction model based on dependent Markov chain is then constructed. A cross-adaptive gray wolf optimization algorithm improved by adaptive position adjustment strategy and cross-optimal solution strategy is subsequently developed to optimize the prediction model. Simulation results using the IEEE 39-BA 110 test system verify the effectiveness and superiority of the proposed method.

Original languageEnglish
Article number9079826
Pages (from-to)82844-82854
Number of pages11
JournalIEEE Access
Publication statusPublished - 2020


  • cross-adaptive grey wolf optimization
  • Cyber-physical system
  • Markov chain
  • risk region prediction

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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