Intelligent electrical power grids, widely referred to as smart grids (SGs), rely on digital technology resources, especially communication and measurement devices, becoming a cyber-physical energy system. Massive data flow between grid elements makes smart grids more vulnerable to cyber-attacks. Power system state estimation (SE)—an essential function of energy management systems—is one of these data integrity attacks’ targets. Integrity validation routines can fail when insufficient redundancy levels are reached, and spurious data occur. These levels are associated with critical data, i.e., those whose unavailability makes the grid unobservable. Data redundancy is a metric that gives a precarious indication that SE can run. Alternatively, it is more appropriate to quantify this function strength concerning its results’ reliability, which can be achieved by criticality analysis (CA). This paper proposes a novel approach to visualize the results of an extensive CA through representative graphs; they facilitate understanding the usefulness of CA. Critical sets of measurements are generalized, and several metrics are proposed to reveal measuring system vulnerabilities, assisting the design of protection schemes to resist cyber-attacks. Simulations attained on the IEEE-30bus system evince significant improvements in the interpretation/use of CA.

中文翻译：

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关键数据可视化以增强状态估计的保护方案

智能电网，广泛称为智能电网（SG），依靠数字技术资源，特别是通信和测量设备，成为信息物理能源系统。网格元素之间的大量数据流使智能电网更容易受到网络攻击。电力系统状态估计 (SE)——能源管理系统的一项基本功能——是这些数据完整性攻击的目标之一。当冗余级别不足并出现虚假数据时，完整性验证例程可能会失败。这些级别与关键数据相关联，即那些因不可用而导致网格无法观察的数据。数据冗余是一种指标，它给出了 SE 可以运行的不确定指示。或者，更合适的方法是根据其结果的可靠性来量化此功能强度，这可以通过关键性分析（CA）来实现。本文提出了一种通过代表性图表可视化广泛 CA 结果的新方法；它们有助于理解 CA 的用途。概括了关键的测量集，并提出了几个指标来揭示测量系统的漏洞，协助设计保护方案以抵御网络攻击。在 IEEE-30bus 系统上获得的模拟表明在 CA 的解释/使用方面有显着改进。协助设计抵御网络攻击的保护方案。在 IEEE-30bus 系统上获得的模拟表明在 CA 的解释/使用方面有显着改进。协助设计抵御网络攻击的保护方案。在 IEEE-30bus 系统上获得的模拟表明在 CA 的解释/使用方面有显着改进。