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

Low Voltage Prediction Based on Spark and Ftrl

Authors
Chen Gao, Zhongan Ding, Shengteng Yan, Hongkun Mai
Corresponding Author
Chen Gao
Available Online June 2017.
DOI
https://doi.org/10.2991/ammee-17.2017.34How to use a DOI?
Keywords
Ftrl, Low voltage, Distributed, Big data.
Abstract
"Big data" is a popular keyword in recent years, and data related to the power grid mainly character huge amount, high complexity and broad sources. As low voltage has great influence on normal daily power utilization, besides good real-time monitoring and exception handling, a model based on big-data algorithms and multi-dimensional characters is also needed for real-time prediction. This paper advances a FTRL algorithm based on the Spark framework, and establishes a highly fault-tolerant, real-time, accurate and fast low-voltage prediction system by setting up a FTRL real-time computation platform based on effective characters extracted from a huge amount of voltage data. It can be seen through analysis of experimental results that this system is able to effectively predict low voltage and give alarms, which shows great improvement over the current manual monitoring mechanism.
Open Access
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
ISSN
2352-5401
DOI
https://doi.org/10.2991/ammee-17.2017.34How 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  - Chen Gao
AU  - Zhongan Ding
AU  - Shengteng Yan
AU  - Hongkun Mai
PY  - 2017/06
DA  - 2017/06
TI  - Low Voltage Prediction Based on Spark and Ftrl
BT  - Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/ammee-17.2017.34
DO  - https://doi.org/10.2991/ammee-17.2017.34
ID  - Gao2017/06
ER  -