Real-time system for automatic classification of power quality disturbances

This paper presents an automatic system, implemented in LabVIEW, for real-time classification of electrical disturbances. Its basic structure can be listed in three stages: signal acquisition, feature extraction and final classification. The first one refers to the signal sampling by means of a monitoring embedded system and to filtering through a notch filter in order to divide the data into a fundamental component and an error signal. The RMS value and the second-order cumulants are extracted from the fundamental component and the error signal, respectively. The extracted features are then sent to the classification process, which is based on a decision tree constructed with perceptrons and a Bayesian classifier. It was possible to classify twenty classes of multiple and isolated disturbances. The results were satisfactory in which a classification accuracy of 98.47% was achieved for signals simulated through arbitrary waveform generator.