An Adaptive Approach for Voltage Sag Automatic Segmentation

Voltage sag characterization is essential for extracting information about a sag event’s origin and how sag events impact sensitive equipment. In response to such needs, more characteristics are required, such as the phase-angle jump, point-on-wave, unbalance, and sag type. However, the absence of an effective automatic segmentation method is a barrier to obtaining these characteristics. In this paper, an automatic segmentation method is proposed to improve this situation. Firstly, an extended voltage sag characterization method is described, in which segmentation plays an important role. Then, a multi-resolution singular value decomposition method is introduced to detect the boundaries of each segment. Further, the unsolved problem of how to set a threshold adaptively for different waveforms is addressed, in which the sag depth, the mean square error, and the entropy of the sag waveform are considered. Simulation data and field measurements are utilized to validate the effectiveness and reliability of the proposed method. The results show that the accuracies of both boundary detection and segmentation obtained using the proposed method are higher than those obtained using existing methods. In general, the proposed method can be implemented into a power quality monitoring system as a preprocess to support related research activities.