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Found 8 papers, 4 papers with code
Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow
Optimal Power Flow (OPF) is a valuable tool for power system operators, but it is a difficult problem to solve for large systems.
Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees
Machine learning algorithms, especially Neural Networks (NNs), are a valuable tool used to approximate non-linear relationships, like the AC-Optimal Power Flow (AC-OPF), with considerable accuracy -- and achieving a speedup of several orders of magnitude when deployed for use.
Physics Informed Neural Networks for Phase Locked Loop Transient Stability Assessment
However, using EMT simulations or Reduced-order models (ROMs) to accurately determine the ROA is computationally expensive.
Minimizing Worst-Case Violations of Neural Networks
For safety-critical systems such as power systems, this places a major barrier for their adoption.
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations
To do so, we demonstrate that the Power Transfer Distribution Factors (PTDF) -- a physics-based linear sensitivity index -- can be derived from the SHAP values.
Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represents a challenging paradigm for the power system operators who are tasked with maintaining grid stability and security in the face of such changes.
Physics-Informed Neural Networks for AC Optimal Power Flow
This paper introduces, for the first time to our knowledge, physics-informed neural networks to accurately estimate the AC-OPF result and delivers rigorous guarantees about their performance.
Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow
Physics-informed neural networks exploit the existing models of the underlying physical systems to generate higher accuracy results with fewer data.
