A Novel Power Quality Event Classification using Slantlet Transform and Fuzzy Logic

A novel system for power quality (PQ) disturbance (event) classification under noisy environment is proposed using slantlet transform (SLT) and fuzzy logic based classifier. SLT is used for extraction of inherent features called as slantlet feature (SF) through time-frequency analysis of PQ events. It is found to be an effective tool for better time localization compared to wavelet transform and extraction of appropriate and informative features from signal under noisy environment, which enhances the classification accuracy. These features are thus utilized for improved classification using fuzzy logic in this paper. We have used fuzzy product aggregation reasoning rule based classifier in the proposed method. Varieties of PQ events which include voltage sag, swell, momentary interruption, notch, oscillatory transient and spikes are considered to test the performance. Comparative simulation studies revealed the superiority of the proposed method compared to SF and wavelet feature based three other fuzzy classifiers under noisy environment.