Abstract
Since state estimation is a real-time application program, its computational speed is important as well as accuracy. Therefore, improving the computation speed of state estimation is a very significant issue. This paper studied the method of improving computational speed of state estimation by using the parallel computing technique. In order to apply the parallel computing technique to state estimation, OpenMP was used as a programming tool. This tool is a shared memory programming model that requires no other devices such as GPU(graphics processing unit) and DSP(digital signal processor), and has the advantage of the fastest data transfer rate between memories. This paper proposes an algorithm that divides the entire system into small scales and performs state estimation in parallel with the divided systems. The proposed algorithm can improve the speed of computing state estimation and also the performance of the bad data processing that may happen for some reasons.
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