Prediction of Icing Thickness on Transmission Lines using ANFIS model

DOI

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.

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/).

Download article (PDF)

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  - Proceedings of the 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  - 10.2991/gcmce-17.2017.7
ID  - Yuan2017/06
ER  -