The simulation of power system dynamics poses a computationally expensive task.
Considering the growing uncertainty of generation and demand patterns, thousands of …

Abstract Machine learning (ML) for real-time security assessment requires a diverse training
database to be accurate for scenarios beyond historical records. Generating diverse …

A significant increase in renewable energy production is necessary to achieve the UN's net-
zero emission targets for 2050. Using power-electronic controllers, such as Phase Locked …

Machine learning (ML) algorithms are remarkably good at approximating complex non-
linear relationships. Most ML training processes, however, are designed to deliver ML tools …

Machine learning can generate black-box surrogate models which are both extremely fast
and highly accurate. Rigorously verifying the accuracy of these black-box models, however …

New generations of power systems, containing high shares of renewable energy resources,
require improved data-driven tools which can swiftly adapt to changes in system operation …

The number of sensors deployed in power systems unlocks the promising benefits and
capabilities of digitalisation. Sensor-driven applications and communication technologies …

This paper provides a systematic investigation into the various nonlinear objective functions
which can be used to explore the feasible space associated with the optimal power flow …

Power grids are becoming more complex to operate in the digital age given the current
energy transition to cope with climate change. As a result, real-time decision-making is …

Machine learning algorithms, especially Neural Networks (NNs), are a valuable tool used to
approximate non-linear relationships, like the AC-Optimal Power Flow (AC-OPF), with …