Data-driven Power System Methods

Last updated on Nov 8, 2021
Photo by Pietro Jeng on Unsplash

Summary

In this research theme, you will find work undertaken by our lab in collaboration with many other institutes in the use data-driven methods for the analysis, control design, and planning of future power systems.

Machine Learning data-driven themes

Publications

Decentralized control in active distribution grids via supervised and reinforcement learning
While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of …
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Detection of Oscillatory Modes in Power Systems using Empirical Wavelet Transform
In electric power systems, detecting inter-area oscillations is crucial to the system operators for maintaining the security of the …
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Experimental Verification of Self-Adapting Data-Driven Controllers in Active Distribution Grids
Lately, data-driven algorithms have been proposed to design local controls for Distributed Generators (DGs) that can emulate the …
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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 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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Optimized local control schemes for active distribution grids using machine learning techniques
Modern distribution system operators are facing a changing scenery due to the increasing penetration of distributed energy resources, …
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