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

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arXiv · 2005.06986

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

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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.

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Zhaoyang Qu, Qianhui Xie, Yuqing Liu, Yang Li, Lei Wang, Pengcheng Xu, Yuguang Zhou, Jian Sun, Kai Xue, Mingshi Cui. 2020-04-29. Power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization. https://doi.org/10.1109/access.2020.2991075

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