Application of parallel particle swarm optimization on power system state estimation

In power system operations, state estimation plays an important role in security control. For the state estimation problem, the weighted least squares (WLS) method is widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as particle swarm optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used particle swarm optimization (PSO) to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. The proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.