Knowledge Discovery in the Smart Grid - A Machine Learning Approach

Aldo Dagnino

2012

Abstract

The increased availability of cheaper sensing technologies, the implementation of fibre-optic networks, the availability of cheaper data storage repositories, and development of powerful machine learning models are fundamental components that provide a new facet to the concept of the Smart Power Grid. An important element in the Smart Grid concept is predicting potential fault events in the Smart Power Grid, or better known as fault prognostics. This paper discusses an approach that uses machine learning methods to discover fault event-related knowledge from historical data and helps in the prognostics of fault events in power grids and critical and expensive components such as power transformers circuit breakers, and others.

Paper Citation

in Harvard Style

Dagnino A. (2012). Knowledge Discovery in the Smart Grid - A Machine Learning Approach . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 366-369. DOI: 10.5220/0004144303660369

in Bibtex Style

@conference{kdir12,
author={Aldo Dagnino},
title={Knowledge Discovery in the Smart Grid - A Machine Learning Approach},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={366-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004144303660369},
isbn={978-989-8565-29-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - Knowledge Discovery in the Smart Grid - A Machine Learning Approach
SN - 978-989-8565-29-7
AU - Dagnino A.
PY - 2012
SP - 366
EP - 369
DO - 10.5220/0004144303660369