Parallel relaxation-based joint dynamic state estimation of large-scale power systems

Massive amounts of data generated in large-scale grids poses a formidable challenge for real-time monitoring of power systems. Dynamic state estimation which is a prerequisite for normal operation of power systems involves the time-constrained solution of a large set of equations which requires significant computational resources. In this study, an efficient and accurate relaxation-based parallel processing technique is proposed in the presence of phasor measurement units. A combination of different types of parallelism is used on both single and multiple graphic processing units to accelerate large-scale joint dynamic state estimation simulation. The estimation results for both generator and network states verify that proper massive-thread parallel programming makes the entire implementation scalable and efficient with high accuracy.