Despite the ability of the traditional weighted least absolute value (WLAV) robust state estimation based on the interior point method (IPM) to improve the estimation accuracy by minimizing the impact of bad data

its practical application is limited because of low efficiency and complex model. A method based on variable substitution IPM is presented that has the nonlinear measurement equations decomposed into two linear equations and two nonlinear transformations by introducing auxiliary variables and the development of two linear WLAV estimation models. The linear WLAV robust state estimation model based on IPM is able to increase the sparsity of the coefficient matrix without calculation of Hessian matrix while reducing the matrix dimension to increase the state estimation speed. Simulation results based on Institute of Electrical and Electronics Engineers (IEEE) benchmark system

Polish grid network and a provincial grid of China demonstrate the effectiveness of the proposed method. Compared with traditional WLAV robust estimation based on IPM

the proposed approach is better able to improve computation speed with good prospects for engineering application. This work is supported by State Grid Corporation of China and National Natural Science Foundation of China (No. 51107032

No. 51277052

No. 61104045).