Parallelization of The Two-Stage State Estimation Method Using GPU-Based Parallel Computing

In this paper, a two-stage state estimation algorithm is presented where operations are performed on a CPU-GPU platform. The state estimator used in this work consists of two stages: in a first stage, the conventional weighted least squares formulation is used and finally, in the second stage, the estimation process is carried out again with the PMU measurements and the vector of estimated states as a result of the first stage. The execution time of the two-stage state estimation algorithm is optimized through the use of parallel computing based on Graphics Processing Units (GPUs). From this approach, the parallel algorithm proposed in this work is 5.74 times faster than its sequential counterpart.