Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)

Research on Emergency Judgement of Low Voltage Area Based on Classification Technique

Authors
Xiaoqiang Zhong
Corresponding Author
Xiaoqiang Zhong
Available Online June 2017.
DOI
https://doi.org/10.2991/ammee-17.2017.35How to use a DOI?
Keywords
low voltage, emergency, classification technique, area.
Abstract
As an important evaluation index of power quality, voltage is the basic condition for guaranteeing power supply service. With the development of industry and the improvement of the living standards, the demand of electricity increases. Low voltage has become a major problem affecting the work, life and entertainment of the people. There are many causes for the low voltage. According to the sudden degree of maintenance and overhaul, such causes can be divided into sudden ones and non-sudden ones. It is of great significance to the daily maintenance of the power grid if the sudden cause can be quickly determined and the maintenance personnel can be sent for site maintenance. In this paper, the classification technique of machine learning is used to analyze the voltage related data in the power grid, and then a method for determining the cause of low voltage in distribution network areas is put forward.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
Part of series
Advances in Engineering Research
Publication Date
June 2017
ISBN
978-94-6252-350-0
DOI
https://doi.org/10.2991/ammee-17.2017.35How 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  - Xiaoqiang Zhong
PY  - 2017/06
DA  - 2017/06
TI  - Research on Emergency Judgement of Low Voltage Area Based on Classification Technique
BT  - Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/ammee-17.2017.35
DO  - https://doi.org/10.2991/ammee-17.2017.35
ID  - Zhong2017/06
ER  -