When applied to a real-world safety critical system like the power grid, general machine learning methods suffer from expensive training, non-physical solutions, and limited interpretability.
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.
Increased levels of randomness and variability are introducing new uncertainties into power systems that can impact system operability and reliability.
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.
Using this grid model, the state estimation is formulated as a Linear Programming (LP) problem whose solution includes a sparse vector of noise terms, which localizes suspicious wrong status and bad data separately.
Quantifying the impact of inverter-based distributed generation (DG) sources on power-flow distribution system cases is arduous.
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 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.
Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs?