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With the deep coupling between the cyber side and the physical side of power systems, the failure of any link of both sides may lead to power outages, so it is necessary to analyze their vulnerability and vulnerable links for targeted improvement of systems. By dynamically attacking the coupled network nodes, this paper proposes a multilevel model and node-to-edge cyber-physical power system and the corresponding indexes system to analyze the vulnerability of the coupled power grid and its key components. The results showed that in the order of the indexes proposed in this paper, attacking surviving power nodes and cyber nodes results in a network crash rate of 25.0% and 66.7% faster than that in the order of "betweenness" and that attacking surviving cyber nodes results in a network crash rate of 89.4% faster than that in the order of "degree." In terms of attacking power nodes, the index proposed in this paper has the same rate as "degree." Therefore, the proposed model can better describe the vulnerability of the power grid to withstand attacks. [ABSTRACT FROM AUTHOR]

Sensing and measurement systems are quintessential to the safe and reliable operation of electric power grids. Their strategic placement is of ultimate importance because it is not economically viable to install measurement systems on every node and branch of a power grid, though they need to be monitored. An overwhelming number of strategies have been developed to meet oftentimes multiple conflicting objectives. The prime challenge in formulating the problem lies in developing a heuristic or an optimisation model that, though mathematically tractable and constrained in cost, leads to trustworthy technical solutions. Further, large‐scale, long‐term deployments pose additional challenges because the boundary conditions change as technologies evolve. For instance, the advent of new technologies in sensing and measurement, as well as in communications and networking, might impact the cost and performance of available solutions and shift initially set conditions. Also, the placement strategies developed for transmission grids might not be suitable for distribution grids, and vice versa, because of unique characteristics; therefore, the strategies need to be flexible, to a certain extent, because no two power grids are alike. Despite the extensive literature on the present topic, the focus of published works tends to be on a specific subject, such as the optimal placement of measurements to ensure observability in transmission grids. There is a dearth of work providing a comprehensive picture for developing optimal placement strategies. Because of the ongoing efforts on the modernisation of electric power grids, there is a need to consolidate the status quo while exposing its limitations to inform policymakers, industry stakeholders, and researchers on the research‐and‐development needs to push the boundaries for innovation. Accordingly, this paper first reviews the state‐of‐the‐art considering both transmission and distribution grids. Then, it consolidates the key factors to be considered in the problem formulation. Finally, it provides a set of perspectives on the measurement placement problem, and it concludes with future research directions. [ABSTRACT FROM AUTHOR]

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin. [ABSTRACT FROM AUTHOR]