Detecting faults in electrical power grids is of paramount importance, both from the electricity
operator and consumer point of view. Modern electric power grids (smart grids) are …

Modeling and recognizing faults and outages in a real-world power grid is a challenging
task, in line with the modern concept of Smart Grids. The availability of Smart Sensors and …

The worldwide power grid can be thought as a System of Systems deeply embedded in a
time-varying, non-deterministic and stochastic environment. The availability of ubiquitous …

Modeling and recognizing events in complex systems through machine learning techniques
is a challenging task. Especially if the model is constrained to be explainable and …

The ability of artificial intelligence and machine learning techniques in classification and
detection of the types of data in large datasets lead to their popularity among scientists and …

With the increased complexity of power systems and the high integration of smart meters,
advanced sensors, and highlevel communication infrastructures within the modern power …

Effective fault detection, classification, and localization are vital for smart grid self-healing
and fault mitigation. Deep learning has the capability to autonomously extract fault …

Unscheduled power disturbances cause severe consequences both for customers and grid
operators. To defend against such events, it is necessary to identify the causes of …

Nowadays, power grid monitoring systems are shifting towards more disseminating and
distributive operations. The diagnosis of faults using knowledge discovery techniques has …

Electrical grids are more dependable, secure, and significant smart grid (SG) technologies.
For effective and dependable electricity distribution, new risks are raised by its high reliance …