Power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization
- Resource Type
- Working Paper
- Authors
- Qu, Zhaoyang; Xie, Qianhui; Liu, Yuqing; Li, Yang; Wang, Lei; Xu, Pengcheng; Zhou, Yuguang; Sun, Jian; Xue, Kai; Cui, Mingshi
- Source
- IEEE Access 8 (2020) 82844-82854
- Subject
- Computer Science - Networking and Internet Architecture
Electrical Engineering and Systems Science - Signal Processing
- Language
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.
Comment: Accepted by IEEE Access