Search Results for author:
Found 13 papers, 5 papers with code
Integrating Physics-Informed Neural Networks into Power System Dynamic Simulations
This paper introduces the first natural step to remove the barriers for using PINNs in time-domain simulations: it proposes the first method to integrate PINNs in conventional numerical solvers.
Correctness Verification of Neural Networks Approximating Differential Equations
Finally, for the first time, we tackle the problem of bounding an NN function without a priori knowledge of the output domain.
Error estimation for physics-informed neural networks with implicit Runge-Kutta methods
We find that this error estimate highly correlates with the NN's prediction error and that increasing the order of the IRK method improves this estimate.
PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks
To accelerate these simulations, we propose a simulator -- PINNSim -- that allows to take significantly larger time steps.
Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility
Comparing the prediction of PINNs to the solution of conventional solvers, we find that PINNs can be 10 to 1000 times faster than conventional solvers.
Bayesian Physics-Informed Neural Networks for Robust System Identification of Power Systems
Bayesian Physics-Informed Neural Networks (BPINNs) combine the advantages of Physics-Informed Neural Networks (PINNs), being robust to noise and missing data, with Bayesian modeling, delivering a confidence measure for their output.
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.
Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation
In order to drastically reduce the heavy computational burden associated with time-domain simulations, this paper introduces a Physics-Informed Neural Network (PINN) to directly learn the solutions of power system dynamics.
Transient Stability Analysis with Physics-Informed Neural Networks
Physics-informed neural networks are different: they incorporate the power system differential algebraic equations directly into the neural network training and drastically reduce the need for training data.
Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers
In the second approach, we model the Newton solver at the heart of an implicit Runge-Kutta integrator as a contracting map iteratively seeking a fixed point on a time domain trajectory.
Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization
The work proposed in this paper uses physics-informed neural networks to capture the power system dynamic behavior and, through an exact transformation, converts them to a tractable optimization problem which can be used to determine critical system indices.
Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics
Varying power-infeed from converter-based generation units introduces great uncertainty on system parameters such as inertia and damping.
