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

doi:10.1109/ACCESS.2020.2991075

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 journal › Article › peer-review

21 Citations (SciVal)

Abstract

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 language	English
Article number	9079826
Pages (from-to)	82844-82854
Number of pages	11
Journal	IEEE Access
Volume	8
DOIs	https://doi.org/10.1109/ACCESS.2020.2991075
Publication status	Published - 2020

Bibliographical note

Funding Information:
This work was supported in part by the Key Projects of the National Natural Science Foundation of China under Grant 51437003, and in part by the Jilin Science and Technology Development Plan Project of China under Grant 20160623004T and Grant 20180201092GX.

Publisher Copyright:
© 2013 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Funding

This work was supported in part by the Key Projects of the National Natural Science Foundation of China under Grant 51437003, and in part by the Jilin Science and Technology Development Plan Project of China under Grant 20160623004T and Grant 20180201092GX.

Keywords

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

ASJC Scopus subject areas

General Computer Science
General Materials Science
General Engineering

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10.1109/ACCESS.2020.2991075Licence: CC BY

Cite this

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

abstract = "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.",

keywords = "cross-adaptive grey wolf optimization, Cyber-physical system, Markov chain, risk region prediction",

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

note = "Funding Information: This work was supported in part by the Key Projects of the National Natural Science Foundation of China under Grant 51437003, and in part by the Jilin Science and Technology Development Plan Project of China under Grant 20160623004T and Grant 20180201092GX. Publisher Copyright: {\textcopyright} 2013 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2020",

doi = "10.1109/ACCESS.2020.2991075",

language = "English",

volume = "8",

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T1 - Power cyber-physical system risk area prediction using dependent markov chain and improved grey wolf optimization

AU - Qu, Zhaoyang

AU - Xie, Qianhui

AU - Liu, Yuqing

AU - Li, Yang

AU - Wang, Lei

AU - Xu, Pengcheng

AU - Zhou, Yuguang

AU - Sun, Jian

AU - Xue, Kai

AU - Cui, Mingshi

N1 - Funding Information: This work was supported in part by the Key Projects of the National Natural Science Foundation of China under Grant 51437003, and in part by the Jilin Science and Technology Development Plan Project of China under Grant 20160623004T and Grant 20180201092GX. Publisher Copyright: © 2013 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - cross-adaptive grey wolf optimization

KW - Cyber-physical system

KW - Markov chain

KW - risk region prediction

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

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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