基于相关马尔可夫链和改进灰狼优化的电力信息物理系统风险区域预测
Power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization
摘要：
现有的电力网络物理系统（CPS）风险预测结果不准确，因为它们无法反映组件的实际物理特性和特定的运行状态。提出了一种基于相关马尔可夫链的电力CPS风险区域预测方法。首先对非均匀功率CPS耦合网络的负载和约束进行表征，并将其作为节点状态判断的标准。在此基础上，考虑到耦合网络中各组成节点的异构性和相互依赖性，构建了基于相关马尔可夫链的电力CPS风险区域预测模型。通过自适应位置调整策略和交叉最优解策略改进交叉自适应灰狼优化算法，对预测模型进行优化。IEEE 39-BA 110测试系统的仿真结果验证了该方法的有效性和优越性。
英文摘要：
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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