Detection of Disturbances in Voltage Signals for Power Quality Analysis Using HOS

Abstract

This paper outlines a higher-order statistics (HOS)-based technique for detecting abnormal conditions in voltage signals. The main advantage introduced by the proposed technique refers to its capability to detect voltage disturbances and their start and end points in a frame whose length corresponds to, at least, N = 16 samples or 1/16 of the fundamental component if a sampling rate equal to f s = 256 × 60 Hz is considered. This feature allows the detection of disturbances in submultiples or multiples of one-cycle fundamental component if an appropriate sampling rate is considered. From the computational results, one can note that almost all abnormal and normal conditions are correctly detected if N = s256, 128, 64, 32, and 16 and the SNR is higher than 25 dB. In addition, the proposed technique is compared to a root mean square (rms)-based technique, which was recently developed to detect the presence of some voltage events as well as their sources in a frame whose length ranges from 1/8 up to one-cycle fundamental component. The numerical results reveal that the proposed technique shows an improved performance when applied not only to synthetic data, but also to real one.

A Dual Monitoring Technique to Detect Power Quality Transients Based on the Fourth-Order Spectrogram

“…Within the transient detection context, the target PQ event is always considered non-Gaussian, while the floor is assumed to be stationary Gaussian signal [4,[22][23][24]. Thus, using HOS would help to locate the transient in a qualitative way, with a rough approximation.…”

HOS network-based classification of power quality events via regression algorithms

“…During the last decade, some researches [7,15,24] have demonstrated the usage of HOS features for PQ monitoring. The motivation of HOS in PQ analysis is twofold.…”

“…In the same direction, Gu and Bollen [6] found relevant characteristics associated to PQ events in the time and frequency domains. The work by Ribeiro et al is also remarkable [7], which extracted new time-domain features based in cumulants. The same authors performed the classification of single and multiple disturbances using HOS in the time domain and Bayes' theory-based techniques [8].…”

A novel instrument for power quality monitoring based in higher-order statistics: a dynamic triggering index for the smart grid

“…In addition, noise degrades the effectiveness of these second-order techniques. Recent studies concluded that a better characterization of symmetry and shape of the waveform are possible via the 3erd and 4th higher-order statistics (HOS), that also account for the nonlinear assessment of the signals [9], [10].…”