Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)

Analysis and Prediction for the Voltage Spike of Power Cable based on the Regression Model

Authors
Wei Xia, Yongbin Bai, Fenghai Ding, Bin Qiu, Shenxiang Gao
Corresponding Author
Wei Xia
Available Online June 2016.
DOI
10.2991/mecs-17.2017.82How to use a DOI?
Keywords
voltage spike, regression model, variance analysis, F-test.
Abstract

In view of the problem that the test of spike signal in the power line is difficult to capture, and based on the theory of time - domain analysis of dynamic circuit, the influence factors of voltage spike test by using the maximum value of traditional multi - random test are studied. By means of the statistical analysis of the test data, a voltage spike prediction method based on linear regression model is proposed. According to the principle of correlation test of variance analysis, the validity of the regression model is verified. The accuracy of the prediction results is evaluated.

Copyright
© 2017, 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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Volume Title
Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
Series
Advances in Engineering Research
Publication Date
June 2016
ISBN
978-94-6252-352-4
ISSN
2352-5401
DOI
10.2991/mecs-17.2017.82How to use a DOI?
Copyright
© 2017, 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/).

Cite this article

TY  - CONF
AU  - Wei Xia
AU  - Yongbin Bai
AU  - Fenghai Ding
AU  - Bin Qiu
AU  - Shenxiang Gao
PY  - 2016/06
DA  - 2016/06
TI  - Analysis and Prediction for the Voltage Spike of Power Cable based on the Regression Model
BT  - Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecs-17.2017.82
DO  - 10.2991/mecs-17.2017.82
ID  - Xia2016/06
ER  -