An interior point method for power system weighted nonlinear L1 norm static state estimation

This paper presents a new interior point algorithm to solve power system weighted nonlinear L₁ norm state estimation problems (IPWNL₁). On the basis of the perturbed Karush-Kuhn-Tucker (KKT) conditions of the primal problem, the authors derive the IPWNL₁ algorithm for solving the state estimation problems. Compared with the sequential linear programming approach and logarithmic barrier function method, the proposed IPWNL₁ algorithm possesses an excellent convergence property. That is, the number of iterations until convergence is roughly constant with power system size and measurement redundancy and mostly less than 10. Moreover, it has another valuable property that the convergence of the algorithm is quite insensitive to changes in weighting factors. To greatly enhance the computational efficiency, two schemes of the correction equation are proposed which have been realized by the rearrangement of the correction equation. Computer simulation experiments on test systems, which range in size from 5 to 1047 buses, have shown that the proposed algorithm has reached the level of practical applications due to its fast and robust property