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Found 42 papers, 13 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.
Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systems
The BPINN combines the advantages of Physics-informed Neural Networks (PINNs), such as inverse problem applicability, with Bayesian approaches for uncertainty quantification.
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.
Topology-Aware Neural Networks for Fast Contingency Analysis of Power Systems
Training Neural Networks able to capture the topology changes of the power grid is one of the significant challenges towards the adoption of machine learning techniques for N-k security computations and a wide range of other operations that involve grid reconfiguration.
GPU-Accelerated Verification of Machine Learning Models for Power Systems
Our contributions achieve a speedup that can exceed 100x and allow higher degrees of verification flexibility.
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.
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.
Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks
According to non-incremental control rules, such as the one mandated by the IEEE Standard 1547, the reactive power setpoint of each DER is computed as a piecewise-linear curve of the local voltage.
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.
Minimizing Worst-Case Violations of Neural Networks
For safety-critical systems such as power systems, this places a major barrier for their adoption.
Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning
This task of optimal rule design (ORD) is challenging as Volt/VAR rules introduce nonlinear dynamics, and lurk trade-offs between stability and steady-state voltage profiles.
Global Performance Guarantees for Neural Network Models of AC Power Flow
This paper develops for the first time, to our knowledge, a tractable neural network verification procedure which incorporates the ground truth of the non-linear AC power flow equations to determine worst-case neural network performance.
Optimal Design of Volt/VAR Control Rules for Inverter-Interfaced Distributed Energy Resources
The IEEE 1547 Standard for the interconnection of distributed energy resources (DERs) to distribution grids provisions that smart inverters could be implementing Volt/VAR control rules among other options.
Emission-Aware Optimization of Gas Networks: Input-Convex Neural Network Approach
Gas network planning optimization under emission constraints prioritizes gas supply with the least CO$_2$ intensity.
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.
A Novel Decentralized Inverter Control Algorithm for Loss Minimization and LVRT Improvement
Algorithms that adjust the reactive power injection of converter-connected RES to minimize losses may compromise the converters' fault-ride-through capability.
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.
Reliability-Aware Probabilistic Reserve Procurement
The proposed probabilistic reserve procurement allows restricted reserve providers to enter the market, thereby increases liquidity and has the potential to lower procurement costs in power systems with high shares of variable renewable energy sources.
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 optimization problems computationally intractable.
Auction-Based vs Continuous Clearing in Local Flexibility Markets with Block Bids
Continuous flexibility markets have the advantage of allowing more liquidity, which can be critical in the earlier stages of such markets, and can be operated closer to real-time, thereby enabling a better use of the latest forecasts; but, by design, they also result to a lower social welfare compared to auction-based markets.
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.
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.
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.
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.
Neural network interpretability for forecasting of aggregated renewable generation
With the rapid growth of renewable energy, lots of small photovoltaic (PV) prosumers emerge.
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.
Towards Optimal Coordination between Regional Groups: HVDC Supplementary Power Control
With Europe dedicated to limiting climate change and greenhouse gas emissions, large shares of Renewable Energy Sources (RES) are being integrated in the national grids, phasing out conventional generation.
Zero-inertia Offshore Grids: N-1 Security and Active Power Sharing
With Denmark dedicated to maintaining its leading position in the integration of massive shares of wind energy, the construction of new offshore energy islands has been recently approved by the Danish government.
Decentralized Model-free Loss Minimization in Distribution Grids with the Use of Inverters
Distribution grids are experiencing a massive penetration of fluctuating distributed energy resources (DERs).
Extended Mathematical Derivations: Decentralized Model-free Loss Minimization in Distribution Grids with the Use of Inverters
This document contains extended mathematical derivations for the communication-free and model-free algorithms that can actively control converter-connected devices, and can operate either as stand-alone or in combination with centralized optimization algorithms.
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.
Market Integration of HVDC Lines: Cost Savings from Loss Allocation and Redispatching
This paper presents two cost benefit analyses on the utilization of HVDC interconnectors in the Nordic countries: in the first we investigate the utilization of HVDC interconnectors for reserve procurement and, in the second, we assess the implementation of implicit grid losses on HVDC interconnectors in the day-ahead market.
Optimization and Control Systems and Control Systems and Control
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.
Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow
This paper introduces a framework to capture previously intractable optimization constraints and transform them to a mixed-integer linear program, through the use of neural networks.
Physics-Informed Neural Networks for Power Systems
This work unlocks a range of opportunities in power systems, being able to determine dynamic states, such as rotor angles and frequency, and uncertain parameters such as inertia and damping at a fraction of the computational time required by conventional methods.
Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications
This paper presents for the first time, to our knowledge, a framework for verifying neural network behavior in power system applications.
Deep Learning for Power System Security Assessment
Security assessment is among the most fundamental functions of power system operator.
Efficient Database Generation for Data-driven Security Assessment of Power Systems
This paper proposes a modular and highly scalable algorithm for computationally efficient database generation.
Systems and Control
