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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.

Prediction of Cable Junction Temperature in Power Transmission System based on BP Neural Network optimized by Genetic Algorithm