MATEC Web Conf.
Volume 232, 20182018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|Number of page(s)||5|
|Section||Algorithm Study and Mathematical Application|
|Published online||19 November 2018|
Prediction of Cable Junction Temperature in Power Transmission System based on BP Neural Network optimized by Genetic Algorithm
College of Mechanical and Electrical Engineering, Jinggangshan University. 343009 Jian Jiangxi, China
2 College of Electronic Engineering, Tianjin University of Technology and Education. 300222 Dagu South Road, Tianjin, China
3 College of Electronic Engineering, Tianjin University of Technology and Education. 300222 Dagu South Road, Tianjin, China
4 Tianjin Navigation Instrument Research Institute. 300131 Tingzigu No.1 Road, Tianjin, China
a Corresponding author: firstname.lastname@example.org
Two forward neural networks were established in this study. Training and learning of reflection factor data and prediction results were conducted respectively then the weights and thresholds of the two networks are optimized by genetic algorithm, finally the set of target values can still be predicted without reflection factor data. In order to predict the temperature of the conductor in the cable joint of a power transmission system, the genetic algorithm is used to optimize the BP neural network to establish an effective prediction model based on the analysis of the related reflection factors. This model not only has the strong learning ability of BP neural network, but also combines the excellent global searching ability of genetic algorithm. The innovation of this research is that the network 1 is used to train the reflective factor data to get the corresponding time point temperature value, and then the reflective factor data of three consecutive time points are trained by the network 2 to get the fourth time point temperature value. The whole process of solving the temperature value of the fourth time point does not need the reflective factor data of the time point.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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