Machine Learning

DENSE
Dependable engineering of Smart Energy Systems
DENSE
Anomaly Detection in Electric Power Systems using Machine Learning Methods
The electric power system is a complex nonlinear system that functions in a dynamic envi- ronment and is frequently subjected to a wide …
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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 …
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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 …
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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 …
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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 …
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