Work

Testing Machine Learned fault Detection and Classification on a DC Microgrid.

Year: 2022

Type: conference-paper

Abstract: The potential for 5 different Machine Learning (ML) classifiers to identify fault type and fault resistance in a DC Microgrid is explored.The ML algorithm are trained using simulated fault data from a 750V Microgrid modelled in PSCAD/EMTDC.The performance of the trained algorithms are tested using real fault data gathered from an operational DC Microgrid.

DOI: 10.2172/2002364

Authors: Samuel Ojetola, Matthew Reno, Jack Flicker, Daniel Bauer, David Stoltzfuz

Institutions: Sandia National Laboratories, Emera (Canada)

Language: English

Topic: Power Systems Fault Detection

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Funders: U.S. Department of Energy, National Nuclear Security Administration, Sandia National Laboratories