Abstract
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.
Highlights
The introductionIn the power transmission system, the cable is very important equipment, and the cable connector is a weak link
Artificial neural network, to some extent, mimics the information processing, storage and retrieval function of the human brain nervous system, which is a kind of simplification, abstraction and Simulation of the neural network of human brain
The Back Propagation (BP) learning algorithm[3] proposed by Rumelhart and others in 1985 is more commonly used
Summary
In the power transmission system, the cable is very important equipment, and the cable connector is a weak link. The sensitivity of cable detection equipment is low and the maintenance cost is high. In order to realize real-time cooling or maintenance treatment and ensure the normal and safe operation of the circuit system, this paper selects some surface touchable temperature reflecting factors to precisely predict the conductor temperature[2]. The Back Propagation (BP) learning algorithm[3] proposed by Rumelhart and others in 1985 is more commonly used. It uses the error of the output to estimate the error of the direct preamble of the output layer, and estimates the error of the previous layer with this error, and the error estimate of all layers can be obtained
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