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A study on development of power grid fault prediction system based on big data and preceding activities to calculate optimal investment cost
Journal of the Korean Data & Information Science Society 2018;29:779-94
Published online May 31, 2018
© 2018 Korean Data and Information Science Society.

Chung-won Lim1 · Sang-kook Han2

1Incheon Regional Headquarters, KEPCO
2Department of Electrical and Electronic Engineering, Yonsei University
Correspondence to: Professor, Department of Electrical and Electronic Engineering, Yonsei University. E-mail: skhan@yonsei.ac.kr
Received April 11, 2018; Revised May 3, 2018; Accepted May 8, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Nowadays, RCM(Reliability Centered Maintenance), which means performing maintenance based on Reliability Analysis, is broadly applied to all over the industries. We also have seen an active utilization of big data in industries especially as advancements in big data processing technology is made. RCM and big data are also applicable to managing distribution facilities but are limited to fault resulted from deterioration only. However, as usage environment or usage level can cause different faults even from the same type of facilities, RCM must be performed in consideration of environmental factors to establish optimal maintenance plan. Currently, in case of domestic distribution facilities in Korea, there are issues to be resolved before applying RCM or big data such as maintenance and fault data system management, track record of environmental factors or more. This research studies the issues that must be settled in advance and the solutions to develop optimal distribution facility management system based on big data and RCM.
Keywords : Big data, distribution facility, maintenance optimization, meteorological elements, reliability analysis.