The topology model of a power grid is the basis of the perception of power grid state. An erroneous topology model will lead to serious deviation of the results of state identification from the actual state of power grid. Thus

topology error identification is of utmost importance. Existing topology error identification methods have inadequacies when applied to large-scale systems and when used to identify bad data and topology error at the same time. Based on the minimum topology error criterion

a mixed-integer programming model is formulated to identify topology error. In the model

the measurement functions are denoted as linear constraints and conic constraints. The products of binary variables and continuous variables are linearized by transforming them to mixed integer inequalities. After linearization

the model can be efficiently solved. Both the branch topology error and the substation topology error can be identified correctly. This work is supported by National Natural Science Foundation of China (No. 51207136).