Abstract：To improve the quality of power distribution network parameters，an abnormal parameter

identification and localization method for distribution networks based on smart meter measurements was proposed.

The method transformed the nonlinear identification equation solving problem in traditional identification

algorithms into the inference problem of the optimal distribution of parameters. On the basis of parameter

identification，probability statistics method was used to locate abnormal parameters. Firstly，given the initial

distribution of line parameters，Markov Chain Monte Carlo method was used to generate parameter samples. The

parameter distribution was updated through tree estimation method and loss function. The expectation of the

parameter distribution when the loss function converges was taken as the identified value of the line parameters.

Secondly，the relative deviation distances of line parameters were calculated，and probability statistics method was

used to judge whether the identified data are bad data or abnormal parameters. The bad data were directly

eliminated. Finally，the abnormal factors causing the incorrect feedback of line parameters were analyzed to locate

the abnormal parameters of the line. The identification process of parameters was demonstrated through an actual

29-node 10 kV feeder. The abnormal parameter location was carried out through an actual 97-node 10 kV feeder，

proving the feasibility and effectiveness of the proposed method.

      Key words：distribution network；parameter identification；abnormal parameter localization；optimal distribution；probability statistics；abnormal factors