Proceedings of the 1st International Conference on Information Technologies in Education and Learning

Voltage Sag Diagnosis Using Big Data Analysis

Authors
Bingbing Zhao, Zhongda Yuan, Junwei Cao, Huaying Zhang, Zhengguo Zhu, Senjing Yao
Corresponding Author
Bingbing Zhao
Available Online March 2016.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 1st International Conference on Information Technologies in Education and Learning
Series
Advances in Computer Science Research
Publication Date
March 2016
ISBN
978-94-6252-168-1
ISSN
2352-538X
DOI
https://doi.org/10.2991/icitel-15.2016.26How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - https://doi.org/10.2991/icitel-15.2016.26
ID  - Zhao2016/03
ER  -