The cyber–physical infrastructure of a smart grid requires data-dependent artificial
intelligence (AI)-based forecasting schemes for predicting different aspects for the short-to …

The monitoring of electrical equipment and power grid systems is very essential and
important for power transmission and distribution. It has great significances for predicting …

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 …

Electric failures are a problem for customers and grid operators. Identifying causes and
localizing the source of failures in the grid is critical. Here, we focus on a specific power grid …

There is a growing interest in applying machine learning methods on large amounts of data
to solve complex problems, such as prediction of events and disturbances in the power …

Power supply disruptions, including short-time disturbances, can lead to large direct and
indirect financial losses. The ability to predict the risk of these disturbances allows for …

We describe a method for assessing the value of additional data sources used in the
prediction of unwanted events (voltage dips, earth faults) in the power system. Using this …

Across the power grid infrastructure, deployed power transmission systems are susceptible
to incipient faults that interrupt standard operations. These incipient faults can range from …

Prediction is the basis of good planning and management in power systems of smart cities,
especially when considering sustainability. The same applies for power quality, that in the …

The power system is changing rapidly, and new tools for predicting unwanted events are
needed to keep a high level of security of supply. Large volumes of data from the Norwegian …