A practical approach for power system state estimation based on the hybrid Particle Swarm Optimization algorithm

Power system dynamic state estimation is an important tool for the analysis, planning, and operation of a power system. In this paper, a new hybrid state estimation method based on the Nelder-Mead (NM) simplex search method and Particle Swarm Optimization (PSO) is proposed. State estimation is an optimization problem including continuous variables, whose objective is to minimize the difference between the calculated and measured values of variables. Here, the NM-PSO hybrid method tries to find the global optima solutions much efficiently in comparison with other artificial intelligence techniques such as genetic algorithm. The two main state estimation methods, namely weighted least square and weighted least absolute value, were used to construct the objective function over which this hybrid stochastic algorithm was applied, and a comparison was made over the data obtained from a case study on the IEEE 14 bus test system for a better understanding of this paper.