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A particle swarm algorirhm for solving the synchronous generator parameters identification on-line problem

Xiuge Zhang, Ye Ren, Qizhou Hu

With high demand about security and stability analysis of power system, to obtain fast and accurate real-time grid model has become important for power system. the paper presents a small population-based particle swarm optimization (SPPSO) method to identify synchronous generator parameters based on the PMU data. Compared with hybrid genetic algorithm to make parameter identification of synchronous generator and got the better result. In this method, the parameters identification of synchronous generator is formulated as an optimization problem of input-output system. A small population-based particle swarm algorithm has less computation, fast convergence speed, the identification accuracy is high, it is suitable for real-time online parameter identification of power system. and the synchronous generator parameters identification becomes easy