Proceedings of the 2017 Global Conference on Mechanics and Civil Engineering (GCMCE 2017)

Prediction of Icing Thickness on Transmission Lines using ANFIS model

Authors
An Yuan, Yao Jiang, Xi Fang, Wangyu Yao, Wei Qian
Corresponding Author
An Yuan
Available Online June 2017.
DOI
https://doi.org/10.2991/gcmce-17.2017.7How to use a DOI?
Keywords
meteorological factors, icing process, short-term prediction, Adaptive Neural Fuzzy Inference System (ANFIS).
Abstract
Icing of transmission line is one of the most serious threats to the safe operation of power system. Its occurrence had brought great losses to the national economy. Therefore, in order to improve the reactive power and anti-risk capability of the icing power grid, it is of great significance to study the icing thickness prediction of transmission lines. In this paper, a short-term prediction method of icing process is proposed. The historical meteorological factors are used as inputs, and combined with the ice thickness increment of sampling points to train and predict the prediction model by using ANFIS. The results show that the predicted result of the short-term prediction method is effectively. And this method has higher prediction accuracy than the widely used BP neural network icing prediction.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 Global Conference on Mechanics and Civil Engineering (GCMCE 2017)
Part of series
Advances in Engineering Research
Publication Date
June 2017
ISBN
978-94-6252-383-8
ISSN
2352-5401
DOI
https://doi.org/10.2991/gcmce-17.2017.7How 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  - An Yuan
AU  - Yao Jiang
AU  - Xi Fang
AU  - Wangyu Yao
AU  - Wei Qian
PY  - 2017/06
DA  - 2017/06
TI  - Prediction of Icing Thickness on Transmission Lines using ANFIS model
BT  - 2017 Global Conference on Mechanics and Civil Engineering (GCMCE 2017)
PB  - Atlantis Press
SP  - 31
EP  - 35
SN  - 2352-5401
UR  - https://doi.org/10.2991/gcmce-17.2017.7
DO  - https://doi.org/10.2991/gcmce-17.2017.7
ID  - Yuan2017/06
ER  -