Parallel computing for smart power oscillation monitoring using synchrophasor measurements

The potential of parallel processing architecture is evaluated for power oscillation monitoring. In the emerging smart grid architecture using Wide Area Monitoring System (WAMS), data collected from Phasor Measurement Units (PMU) in remote locations are transmitted in real-time to the control center. The power system network oscillatory dynamic behavior can then be extracted online using modern signal processing techniques. In this paper an Extended Complex Kalman Filter (ECKF) algorithm is adopted for tracking oscillations. A brief overview of this method along with background of WAMS is presented. Later, parallelism is achieved by decomposing ECKF method into a set of subroutines and distributing them across multiple CPU cores. Comparisons of this performance with a conventional sequential structure is conducted using synthetic signals in MATLAB and Visual C++. The simulation results show that parallel processing is able to reduce the computing time.

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