The application of graph analytics to various domains has yielded tremendous societal and
economical benefits in recent years. However, the increasingly widespread adoption of …

In recent years, the frequent occurrences of large-scale blackouts highlight the necessity of
enhancing the robustness of power grids. In this brief, based on the cascading failure model …

False data injection attacks (FDIAs) pose a significant threat to smart power grids. Recent
efforts have focused on developing machine learning (ML)-based defense strategies against …

Synthetic power systems that imitate functional and statistical characteristics of the actual
grid have been developed to promote researchers' access to public system models …

The integrated energy system (IES) can significantly improve energy efficiency. However, it
introduces multiple operational risks. To improve the reliability of IES, the critical …

Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies
on labeled measurement data for training and testing. The majority of existing detectors are …

To introduce more automation in the distribution level of the power system, increasingly
more data are needed to serve as input to control algorithms. To examine the complex …

Atmospheric mercury is a crucial pollutant that must be well‐controlled to avoid damaging
public health. It is thus necessary to understand from multiple perspectives the roles played …

Future smart grids represent interdependent cyber-physical systems that involve multiple
interactions between a power grid and a communication network. Such a mutual …

Since the introduction of small-world and scale-free properties, there is an ongoing
discussion on how certain real-world networks fit into these network science categories …