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Found 14 papers, 2 papers with code
Towards Hyperparameter-Agnostic DNN Training via Dynamical System Insights
We present a stochastic first-order optimization method specialized for deep neural networks (DNNs), ECCO-DNN.
An Equivalent Circuit Approach to Distributed Optimization
In this work, we introduce a new centralized distributed optimization algorithm (ECADO) inspired by an equivalent circuit model of the distributed problem.
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning
Recent years have seen a rich literature of data-driven approaches designed for power grid applications.
ECCO: Equivalent Circuit Controlled Optimization
To find the value of the critical point, we propose a time step search routine for Forward Euler discretization that controls the local truncation error, a method adapted from circuit simulation ideas.
Towards Practical Physics-Informed ML Design and Evaluation for Power Grid
This work formalizes a new concept of physical interpretability which assesses how a ML model makes predictions in a physically meaningful way and introduces an evaluation methodology that identifies a set of attributes that a practical method should satisfy.
Circuit-theoretic Line Outage Distribution Factor
This work presents the design of AC line outage distribution factor created from the circuit-theoretic power flow models.
Equivalent Circuit Programming for Power Flow Analysis and Optimization
This paper extends the theoretical foundation of Equivalent Circuit Programming to enable the fusion of optimization theory and algorithms with the numerical methods that utilize the domain-specific knowledge of power flow models.
A Risk-Managed Steady-State Analysis to Assess the Impact of Power Grid Uncertainties
Electricity systems are experiencing increased effects of randomness and variability due to emerging stochastic assets.
Adversarially Robust Learning for Security-Constrained Optimal Power Flow
In recent years, the ML community has seen surges of interest in both adversarially robust learning and implicit layers, but connections between these two areas have seldom been explored.
A Convex Method of Generalized State Estimation using Circuit-theoretic Node-breaker Model
An accurate and up-to-date topology is critical for situational awareness of a power grid; however, wrong switch statuses due to physical damage, communication error, or cyber-attack, can often result in topology errors.
Analytical Inverter-Based Distributed Generator Model for Power Flow Analysis
Quantifying the impact of inverter-based distributed generation (DG) sources on power-flow distribution system cases is arduous.
Combined Transmission and Distribution State-Estimation for Future Electric Grids
Instead, a more distributed yet coordinated approach for grid operation and control will emerge that models and analyzes the grid with a larger footprint and deeper hierarchy to unify control of disparate T&D grid resources under a common framework.
Fast AC Steady-State Power Grid Simulation and Optimization Using Prior Knowledge
Fast and accurate optimization and simulation is widely becoming a necessity for large scale transmission resiliency and planning studies such as N-1 SCOPF, batch contingency solvers, and stochastic power flow.
Dynamic Graph-Based Anomaly Detection in the Electrical Grid
Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs?
