GPU-accelerated state estimation for large-scale power systems

Power system state estimation is the basis of online analysis application. The traditional serial calculation mode of state estimation suffers from excessive communication and calculation burden, which is difficult to meet the real-time requirement of power dispatching system. In this paper, the state estimation algorithm is accelerated in parallel by the Graphics Processing Unit (GPU). The principle and algorithm of the fast-decoupled least squares method for state estimation are studied and the complex computing tasks involved are analyzed in detail. Then evaluation of best parallel strategy is used for calculation efficiency. Performance tests are carried out on examples of different scales and a speedup of 4.45 times is obtained on a 9241-bus system.