This project addresses the weight least absolute value (WLAV) state estimator for power system applications. This estimator is more robust than the weighted least squared (WLS) state estimator used at present by most utilities. The WLAV estimator involves solving an L1 norm problem which is usually formulated as a linear programming (LP) problem resulting in relatively large computational times. This work is investigating the use of interior point methods for WLAV state estimation. This involves three research questions. First, there is the question of finding a suitable interior point method. Second, there are numerical considerations and sparsity aspects particular to this problem . Finally, there are practical considerations such as the handling of virtual and psuedo measurements by means of equality and constraints. The research will be performed using a sparse matrix environment known as the Sparse Matrix Manipulation System.
