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Found 10 papers, 2 papers with code
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.
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.
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.
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.
Neural network interpretability for forecasting of aggregated renewable generation
With the rapid growth of renewable energy, lots of small photovoltaic (PV) prosumers emerge.
Priority Lists for Power System Investments: Locating Phasor Measurement Units
Optimization models are often used to support decision making at a given point in time.
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.
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.
