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06/18/2023
GPU-Accelerated Verification of Machine Learning Models for Power Systems
Computational tools for rigorously verifying the performance of large-sc...
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03/23/2023
Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees
Machine learning algorithms, especially Neural Networks (NNs), are a val...
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03/21/2023
Physics Informed Neural Networks for Phase Locked Loop Transient Stability Assessment
A significant increase in renewable energy production is necessary to ac...
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03/17/2023
Solving Differential-Algebraic Equations in Power Systems Dynamics with Neural Networks and Spatial Decomposition
The dynamics of the power system are described by a system of differenti...
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03/15/2023
Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility
The simulation of power system dynamics poses a computationally expensiv...
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01/04/2023
Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks
Volt/VAR control rules facilitate the autonomous operation of distribute...
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12/21/2022
Minimizing Worst-Case Violations of Neural Networks
Machine learning (ML) algorithms are remarkably good at approximating co...
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11/17/2022
Deep Learning for Optimal Volt/VAR Control using Distributed Energy Resources
Given their intermittency, distributed energy resources (DERs) have been...
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11/14/2022
Global Performance Guarantees for Neural Network Models of AC Power Flow
Machine learning can generate black-box surrogate models which are both ...
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09/18/2022
Emission-Aware Optimization of Gas Networks: Input-Convex Neural Network Approach
Gas network planning optimization under emission constraints prioritizes...
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03/14/2022
Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
Deep decarbonization of the energy sector will require massive penetrati...
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10/21/2021
Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment
Nonlinear power flow constraints render a variety of power system optimi...
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10/06/2021
Physics-Informed Neural Networks for AC Optimal Power Flow
This paper introduces, for the first time to our knowledge, physics-info...
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06/28/2021
Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow
Physics-informed neural networks exploit the existing models of the unde...
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06/25/2021
Transient Stability Analysis with Physics-Informed Neural Networks
Solving the ordinary differential equations that govern the power system...
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06/19/2021
Neural network interpretability for forecasting of aggregated renewable generation
With the rapid growth of renewable energy, lots of small photovoltaic (P...
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06/04/2021
Contracting Neural-Newton Solver
Recent advances in deep learning have set the focus on neural networks (...
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06/19/2020
Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
This paper introduces for the first time a framework to obtain provable ...
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04/08/2020
Physics-Informed Neural Networks for Non-linear System Identification applied to Power System Dynamics
Varying power-infeed from converter-based generation units introduces gr...
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03/17/2020
Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow to Mixed-Integer Linear Programs
This paper introduces a framework to capture previously intractable opti...
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11/09/2019
Physics-Informed Neural Networks for Power Systems
This paper introduces for the first time, to our knowledge, a framework ...
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10/03/2019
