Abstract：Accurate topology and line parameter information is the basis of state estimation and security

control of distribution network. Affected by the performance of collection terminals and environmental factors，the

measurement error of metering equipment often deviates from Gaussian distribution. Higher requirements are put

forward for the robustness of topology and parameter identification model under non-Gaussian noise measurements.The mathematical optimization model of distribution network parameter identification was firstly constructed with the goal of minimizing power estimation error. In order to improve the performance of identification algorithm in complex error scenarios，the correntropy induced loss function was established，and an improved correntropy matching pursuit（CMP）algorithm was proposed based on half-quadratic optimization and noise filtering. Finally，the simulation analysis was carried out on IEEE 33- and 85-bus distribution system，and the test results show that the proposed method can correctly identify the topology and effectively estimate the line parameters in both Gaussian and non-Gaussian data noise scenarios.

      Key words：distribution network；topology identification；line parameter identification；correntropy induced

metric（CIM）；correntropy matching pursuit（CMP）