Abstract When Internet of Things (IoT) applications become a part of people's daily life,
security issues in IoT have caught significant attention in both academia and industry …

Today's power systems are seeing a paradigm shift under the energy transition, sparkled by
the electrification of demand, digitalisation of systems, and an increasing share of …

This paper develops a hierarchical deep learning machine (HDLM) to efficiently achieve
both quantitative and qualitative online transient stability prediction (TSP). For the sake of …

HVDC technology is developing fast and HVDC grids are increasingly seen as a possible
and feasible solution to manage the future power system with large amounts of renewables …

This paper proposes a decentralized derivative-free dynamic state estimation method in the
context of a power system with unknown inputs, to address cases when system linearization …

Various supervised machine learning approaches have been used in the past to assess the
power system security (also known as reliability). This is typically done by training a …

Dynamic Security Assessment (DSA) for the future power system is expected to be
increasingly complicated with the higher level penetration of renewable energy sources …

This paper presents a computational platform for dynamic security assessment (DSA) of
large electricity grids, developed as part of the iTesla project. It leverages high performance …

Supervised machine learning has been successfully used in the past to infer a system's
security boundary by training classifiers (also referred to as security rules) on a large …

Probabilistic security assessment and real-time dynamic security assessments (DSA) are
promising to better handle the risks of system operations. The current methodologies of …