Power quality disturbances monitoring using Hilbert-Huang transform and SVM classifier

The voltage signal disturbances may lead to the difficulties viz. heating, device aging, etc. Enhancement of the devalued supplied power voltage is a prime task that needs identification and also the classification of the noise. Therefore, for disturbances namely voltage sag, transients, swell and harmonic voltage disparities, identification by Hilbert-Huang transform and classification by Support Vector Machine is presented. The fault location in the gathered real-time substation data is analyzed by Hilbert transform. Also the performance estimation of Empirical Mode Decomposition with its noise assisted version called Ensemble Empirical Mode Decomposition is presented. The comparison amongst the classification results of cross-correlation and SVM are also proposed.