Real-time system for automatic classification of power quality disturbances

• Published in: 2016 17th International Conference on Harmonics and Quality of Power (ICHQP) pp. 908 - 913
• Main Authors: Ribeiro, E. G., Dias, G. L., Barbosa, B. H. G., Ferreira, D. D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2016
• Subjects: Bayes methods; Decision Tree; Feature extraction; High-Order Statistics; LabVIEW; Monitoring; Notch filters; Power harmonic filters; Power quality; Real-Time Classification; Real-time systems; Smart Grids
• ISSN: 2164-0610
• DOI: 10.1109/ICHQP.2016.7783405

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.