Artificial intelligence and mathematical models of power grids driven by renewable energy sources: A survey
To face the impact of climate change in all dimensions of our society in the near future, the
European Union (EU) has established an ambitious target. Until 2050, the share of …
European Union (EU) has established an ambitious target. Until 2050, the share of …
Deep reinforcement learning for cybersecurity assessment of wind integrated power systems
The integration of renewable energy sources (RES) is rapidly increasing in electric power
systems (EPS). While the inclusion of intermittent RES coupled with the wide-scale …
systems (EPS). While the inclusion of intermittent RES coupled with the wide-scale …
Volt-var optimization in distribution networks using twin delayed deep reinforcement learning
Modern distribution grids are undergoing new challenges due to the stochastic nature of
distributed energy resources (DERs). High penetration of DERs has a significant impact on …
distributed energy resources (DERs). High penetration of DERs has a significant impact on …
Deep reinforcement learning-based volt-var optimization in distribution grids with inverter-based resources
R Hossain, MM Lakouraj… - 2021 North …, 2021 - ieeexplore.ieee.org
High penetration of solar photovoltaic (PV) units in distribution grids and the variability of
their output power have caused new challenges to grid operators. Voltage fluctuations and …
their output power have caused new challenges to grid operators. Voltage fluctuations and …
Low-cost hardware platform for testing ML-based edge power grid oscillation detectors
This paper introduces a low-cost hardware testing platform designed to investigate the
performance of a Machine Learning (ML)-based edge application developed to detect …
performance of a Machine Learning (ML)-based edge application developed to detect …
Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning
LL Pullum - arxiv preprint arxiv:2203.12048, 2022 - arxiv.org
Reinforcement learning has received significant interest in recent years, due primarily to the
successes of deep reinforcement learning at solving many challenging tasks such as …
successes of deep reinforcement learning at solving many challenging tasks such as …
Stochastic Learning and Optimization with Imperfect Data in Cyber-Physical Systems
A Ghasemkhani - 2019 - search.proquest.com
Principal goal of this dissertation is to study stochastic learning and optimization of cyber-
physical systems (CPSs) with imperfect data. CPSs are engineered systems that are built …
physical systems (CPSs) with imperfect data. CPSs are engineered systems that are built …