Knowledge Discovery in the Smart Grid - A Machine Learning Approach

Aldo Dagnino

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Paper

Knowledge Discovery in the Smart Grid - A Machine Learning Approach

Topics: Data Analytics; Machine Learning

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 0IC3K, 366-369, 2012 , Barcelona, Spain

Author: Aldo Dagnino

Affiliation: ABB Corporate Research and ABB Inc., United States

Keyword(s): Machine Learning, Smart Grid, Knowledge Discovery, Fault Prognostics.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

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.

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Paper citation in several formats:

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 (IC3K 2012) - KDIR; ISBN 978-989-8565-29-7; ISSN 2184-3228, SciTePress, pages 366-369. DOI: 10.5220/0004144303660369

@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 (IC3K 2012) - KDIR},
year={2012},
pages={366-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004144303660369},
isbn={978-989-8565-29-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2012) - KDIR
TI - Knowledge Discovery in the Smart Grid - A Machine Learning Approach
SN - 978-989-8565-29-7
IS - 2184-3228
AU - Dagnino, A.
PY - 2012
SP - 366
EP - 369
DO - 10.5220/0004144303660369
PB - SciTePress