Voltage Sag Diagnosis Using Big Data Analysis

DOI

10.2991/icitel-15.2016.26How to use a DOI?

Keywords

power quality; voltage sag; big data; fault location; temporal distribution

Abstract

We propose a data-driven approach for voltage sag diagnosis in this paper. Rather than traditional features such as frequency, amplitude and duration, we apply temporal distribution as a new feature to distinguish whether the sag is caused by power system faults or heavy load switching. Sags caused by these two reasons have a different distribution pattern, and this work interprets it from a number of perspectives. We also perform voltage sag source location by clustering. This approach does not use any physical level analysis and can find the fault source faster as well as accurately.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bingbing Zhao
AU  - Zhongda Yuan
AU  - Junwei Cao
AU  - Huaying Zhang
AU  - Zhengguo Zhu
AU  - Senjing Yao
PY  - 2016/03
DA  - 2016/03
TI  - Voltage Sag Diagnosis Using Big Data Analysis
BT  - Proceedings of the 1st International Conference on Information Technologies in Education and Learning
PB  - Atlantis Press
SP  - 113
EP  - 117
SN  - 2352-538X
UR  - https://doi.org/10.2991/icitel-15.2016.26
DO  - 10.2991/icitel-15.2016.26
ID  - Zhao2016/03
ER  -