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Found 7 papers, 0 papers with code
Tractable Identification of Electric Distribution Networks
In the first method, we use the eigendecomposition of the admittance matrix to generalize the notion of stationarity to electrical signals and demonstrate how the stationarity property can be used to facilitate a maximum a posteriori estimation procedure.
Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control
Machine-learned black-box policies are ubiquitous for nonlinear control problems.
Stability Constrained Reinforcement Learning for Real-Time Voltage Control
Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems.
Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges
With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility.
Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
This paper introduces for the first time a framework to obtain provable worst-case guarantees for neural network performance, using learning for optimal power flow (OPF) problems as a guiding example.
Combining Model-Based and Model-Free Methods for Nonlinear Control: A Provably Convergent Policy Gradient Approach
Model-free learning-based control methods have seen great success recently.
Inverse Power Flow Problem
We show that the admittance matrix can be uniquely identified from a sequence of measurements corresponding to different steady states when every node in the system is equipped with a measurement device, and a Kron-reduced admittance matrix can be determined even if some nodes in the system are not monitored (hidden nodes).
