Voltage Sag Diagnosis Using Big Data Analysis
Bingbing Zhao, Zhongda Yuan, Junwei Cao, Huaying Zhang, Zhengguo Zhu, Senjing Yao
Available Online March 2016.
- https://doi.org/10.2991/icitel-15.2016.26How to use a DOI?
- power quality; voltage sag; big data; fault location; temporal distribution
- 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.
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 -