Detection and Classification of Voltage Variations using Combined Envelope-Neural Network Based Approach
Eyad A. Feilat, Rafat R. Aljarrah, Mohammed B. Rifai |Pages: 112-124|

Abstract— This paper presents a technique for detection and classification of short duration voltage variations including voltage sag, swell and interruption. The detection technique is based on envelope construction using Hilbert transform and classification using artificial neural network. The performance of the classifier is examined over several cases of synthetic voltage variation disturbances. Moreover, the performance of the classifier is tested on a simple distribution system subjected to a single-line-ground fault. The beginning and ending of the disturbance are also estimated. The simulation results show the robust capability of the proposed technique to accurately and rapidly classify voltage variation events.