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Found 10 papers, 1 papers with code
Augmented Digital Twin for Identification of Most Critical Cyberattacks in Industrial Systems
The resulting Augmented Digital Twin (ADT) is then employed in a sequential decision-making optimization formulated to yield the most critical attack scenarios as measured by the defined KPI.
Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flow
This paper presents a novel primal-dual approach for learning optimal constrained DRL policies for dynamic optimal power flow problems, with the aim of controlling power generations and battery outputs.
Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI
The effective representation, precessing, analysis, and visualization of large-scale structured data over graphs are gaining a lot of attention.
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms
This paper proposes a model-free Volt-VAR control (VVC) algorithm via the spatio-temporal graph ConvNet-based deep reinforcement learning (STGCN-DRL) framework, whose goal is to control smart inverters in an unbalanced distribution system.
Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation
Volt-VAR and Volt-Watt control functions are mechanisms that are included in distributed energy resource (DER) power electronic inverters to mitigate excessively high or low voltages in distribution systems.
SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation
Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in the modern distribution power grids.
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
Understanding the Safety Requirements for Learning-based Power Systems Operations
Case studies performed on both voltage regulation and topology control tasks demonstrated the potential vulnerabilities of the standard reinforcement learning algorithms, and possible measures of machine learning robustness and security are discussed for power systems operation tasks.
Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation
Electronic power inverters are capable of quickly delivering reactive power to maintain customer voltages within operating tolerances and to reduce system losses in distribution grids.
Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learning
We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information.
