Failure Probability Metric by Machine Learning for Online Risk Assessment in Distribution Networks Conference Paper

abstract

• 2017 IEEE. The risk assessment approach is useful for monitoring and supervisory control because it provides distribution operator with the capability to quantify the tradeoff between reliability and economic performance. The risk assessment determines the likelihood of something going wrong in a distribution network through the failure probability metric. To deal with the massive variety of information required in the calculation of failure probability we propose a data mining approach. The proposed approach incorporates weather, asset and outage information for characterizing the risk in a distribution network section via GIS platform.

name of conference

• 2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)

authors

• Kezunovic, Mladen

published proceedings

• 2017 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE - LATIN AMERICA (ISGT LATIN AMERICA)

author list (cited authors)

• Leite, J. B., Sanches Mantovani, J. R., Dokic, T., Yan, Q., Chen, P., & Kezunovic, M.

citation count

• 2

complete list of authors

• Leite, Jonatas Boas||Sanches Mantovani, Jose Roberto||Dokic, Tatjana||Yan, Qin||Chen, Po-Chen||Kezunovic, Mladen

publication date

• January 2017

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Electricity Supply Industry
• Failure Probability
• Geographic Information Systems
• Power Distribution
• Risk Analysis

Digital Object Identifier (DOI)

• 10.1109/isgt-la.2017.8126683

International Standard Book Number (ISBN) 13

• 9781538633120

start page

• 1

end page

• 6

volume

• 2017-January

URL

• http://dx.doi.org/10.1109/isgt-la.2017.8126683