Abstract:Accurate line parameters are of great significance to the operation and control of the distribution

network. However，the line parameters of distribution network are affected by environment，working condition and temperature，etc. At the same time，due to the increasing complexity，randomness and volatility of distribution network structure，it is difficult to establish an accurate parameter identification model for the distribution network.A method for identifying the line parameters of the distribution network based on the GA-SVR model was proposed to realize the accurate identification of the line parameters of the distribution network. The genetic algorithm（GA）was used to optimize the penalty factor and kernel function parameters of the support vector regression（SVR）machine，the problem that the default parameters of traditional support vector regression lead to poor model prediction effect was solved. The regression network was trained by using power flow values of distribution network under different impedance parameters，and an improved SVR parameter identification model was constructed to realize the identification of the line parameters of the distribution network. The verification of a 33-node distribution network example shows that the improved SVR parameter identification model can achieve higher-precision distribution network line parameter identification compared with the traditional SVR parameter identification model.

      Key words: distribution network；line parameter identification；genetic algorithm（GA）；support vector

regression（SVR）；parameter optimization