Dynamic parameter identification of generators for smart grid development

After 2003 blackout, wide area measurement, monitoring, and visualization become one of the most important areas of smart grid initiative. Synchrophasors are precise time-synchronized measurements of certain parameters on the electricity grid, now available from grid monitoring devices called phasor measurement units (PMUs). Phasor data and applications are valuable for grid reliability because they give grid operators and planners unprecedented insight into what is happening on the grid at high resolution, over a wide area in time synchronized mode, and where needed, in real-time. Phasor information gives operator the “current” situation of the system. However, engineers still rely on simulation tool to predict the behavior of the power system and provide possible mitigation measures for system problems. The accuracy of the dynamic parameters are the prerequisite for the reliable solutions. Dynamic parameter identification which aims at obtaining accurate dynamic parameters is one of the central topics in power system studies. This paper proposes a hybrid method combining particle swarm optimization (PSO) and sensitivity analysis (SA) for dynamic parameter identification. The proposed hybrid method provides the right balance and trade-off between convergence and computation speed. In addition, the parallel programming is used to take advantage of multiple core processors to significantly increase the computation speed. The simulation results show the validity and benefit of the proposed algorithm.