Machine Learning

DENSE
Dependable engineering of Smart Energy Systems
DENSE
A feature-subspace-based ensemble method for estimating long-term voltage stability margins
This study proposes a methodology for online voltage stability monitoring using a feature subspace based ensemble approach. The overall …
PDF Cite DOI
Clustering Data-driven Local Control Schemes in Active Distribution Grids
Controllable Distributed Energy Resources (DERs) in Active Distribution Grids (ADGs) provide operational flexibility to system …
PDF Cite Project DOI
Data-driven Power System Methods
Research in the area of machine learning for power system applications
Data-driven Power System Methods
Data-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniques
The optimal control of distribution networks often requires monitoring and communication infrastructure, either centralized or …
PDF Cite Project DOI
Data-driven Control Design Schemes in Active Distribution Grids: Capabilities and Challenges
Today, system operators rely on local control of distributed energy resources (DERs), such as photovoltaic units, wind turbines and …
PDF Cite Project DOI