智能电网中稀疏攻击构造与状态估计：集中式与分布式模型
Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models
AI总结：
本文提出利用智能电网网络中出现的稀疏结构的新方法，用于数据注入攻击下的状态估计问题。提出了针对攻击者的攻击构造策略，并设计了优化问题以灵活平衡性能与误报率，同时将集中式模型扩展为分布式框架，通过仿真验证了方法的有效性。
摘要：
本文提出了一种利用智能电网网络中出现的稀疏结构的新方法，用于数据注入攻击下的状态估计问题。首先，针对拥有全部网络信息和节点的攻击者，提出了不可观测稀疏数据注入攻击的构造策略。具体而言，提出了新的优化问题形式，以提供性能与误报率之间的灵活权衡设计。此外，将集中式情况扩展到估计和攻击问题的分布式框架。根据不同的假设，提出了不同的分布式场景，导致资源、网络节点和玩家的分布。因此，为每个提出的框架引入了相应的优化问题，并附带解决该问题的算法。通过在IEEE测试系统中的广泛仿真研究，验证了所提程序在实际环境中的有效性。
英文摘要：
New methods that exploit sparse structures arising in smart grid networks are proposed for the state estimation problem when data injection attacks are present. First, construction strategies for unobservable sparse data injection attacks on power grids are proposed for an attacker with access to all network information and nodes. Specifically, novel formulations for the optimization problem that provide a flexible design of the trade-off between performance and false alarm are proposed. In addition, the centralized case is extended to a distributed framework for both the estimation and attack problems. Different distributed scenarios are proposed depending on assumptions that lead to the spreading of the resources, network nodes and players. Consequently, for each of the presented frameworks a corresponding optimization problem is introduced jointly with an algorithm to solve it. The validity of the presented procedures in real settings is studied through extensive simulations in the IEEE test systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
