Data-driven Power System Methods

Last updated on Dec 12, 2024
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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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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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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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