Characterization of electrical sags and swells using higher-order statistical estimators

This paper deals with the detection of power quality anomalies which preserve the frequency of the power line, in particular sags and swells. Three statistical estimators have been used (variance, skewness and kurtosis) to enhance characterization of these anomalies. The proposed measurement strategy is funded in the tuning of the signal under test via a sliding window over which computation is performed. Then, the calculation of the statistical features reveals the inherent properties of the signal: amplitude, frequency and symmetry. The work primarily examines a number of synthetics in order to extract the theoretical statistical features. Then the algorithm is corroborated using real-life signals, obtaining an accuracy of 83%. This experience is part of the design of an instrument for the measurement of the power quality.

► HOS (skewness and kurtosis) characterize symmetry and harmonic distortion in electrical faults.
► Sag/swell detection is achieved via variance’s surveillance.
► Measurement estimators’ instability targets frequency deviations on the 50-Hz power-line.
► PQ events classification accuracy of 83% over real-life recordings is achieved.