3 papers
eess.SP2024
Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment
Tetiana Bogodorova, Denis Osipov, Luigi Vanfretti
Balanced data is required for deep neural networks (DNNs) when learning to perform power system stability assessment. However, power system measurement data contains relatively few…
cs.LG2020
Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python
Oleh Lukianykhin, Tetiana Bogodorova
The aim of the project is to investigate and assess opportunities for applying reinforcement learning (RL) for power system control. As a proof of concept (PoC), voltage control of…
cs.SE2019
ModelicaGym: Applying Reinforcement Learning to Modelica Models
Oleh Lukianykhin, Tetiana Bogodorova
This paper presents ModelicaGym toolbox that was developed to employ Reinforcement Learning (RL) for solving optimization and control tasks in Modelica models. The developed tool a…