A hybrid method for power system state estimation using Cellular Computational Network

Several heuristic optimization methods including Particle Swarm Optimization (PSO) have been studied for power system state estimation and these perform quite well for small systems. However, in case of larger systems with hundreds of states, these suffer from the curse of dimensionality. To overcome this problem, a hybrid state estimator that consists of a Cellular Computational Network (CCN) and the Genetic Algorithm (GA) is proposed in this study. CCN is a framework that distributes the whole computation to computation cells and the cells execute local estimation. The result of CCN is further improved using GA. To compare the performance of the proposed estimator, two acclaimed variants of PSO, Comprehensive Learning PSO, and Orthogonal Learning PSO, which are specialized in multimodal high dimensional systems, are also implemented both individually and in conjunction with CCN. Through simulation, it is shown that the proposed CCN-GA outperform all direct and hybrid methods in terms of accuracy. Typical results on an IEEE 16-machine 68-bus power system are presented to illustrate the effectiveness of the CCN-GA over other methods.