Singular spectrum analysis based quick online detection of disturbance start time in power grid

Timely detection of the start time and location of disturbance is critical to power grid. The information helps operators quickly catch the disturbance events over wide areas and allows time for taking remedial reactions. In this paper, we proposed to detect the start time point of disturbance using Singular Spectrum Analysis (SSA), which has been proved to be an effective technique in the area of time series analysis for change-point detection. Using the simulation data generated by Power System Tool box, we compared the SSA algorithm with the Event Start Time (EST) algorithm. The experimental results have shown that our SSA algorithm is not only faster and more robust in the noisy environments, but also is able to capture more subtle disturbance that the EST cannot detect.