Detection and Characterization of Time-variant Non-stationary Voltage Sag Waveforms Using Segmented Hilbert Huang Transform

Abstract

The automation of distribution systems will need a fast and efficient signal-processing tool in order to extract useful information from the power quality (PQ) monitors, smart meters, or Internet of Things, which are essential for real-time monitoring, detection, and faster decision-making capability of smart grids. This article proposes the application of the Hilbert–Huang transform (HHT) method for decomposition of the PQ data into its individual frequency components in order to separate the nonstationary voltage sag waveform containing the fundamental frequency component. After the decomposition of the data using the HHT method, an innovative fundamental frequency-based algorithm is proposed for the accurate detection of voltage sag starting and ending times, which are useful for accurate calculation of point-on-wave and voltage sag duration. The proposed algorithm is further extended for automatic detection of the transition segments to accurately calculate the time-varying and single event characteristics such as voltage sag magnitude and phase-angle jump. The composite method is referred to as the segmented Hilbert–Huang transform (SHHT). The limitations of the existing methods of voltage sag detection using the rms method, the fast Fourier transform method, and the waveform envelope method are also outlined in this article. Finally, the results of the detection and characterization of time-variant nonstationary voltage sag data using the proposed SHHT method is presented and discussed.