Detection and classification of power-quality events using discrete Gabor transform and Support Vector Machine

This paper presents a new method for online detection and classification of power quality (PQ) events simultaneously, based on discrete Gabor transform (DGT) and Support Vector Machine (SVM). The features extracted through the DGT and SVM classified PQ events. This method can reduce the features of the disturbance signal significantly, so less time and memory are required for classification by SVM method. Nine types of events are simulated in the classification problem. The simulation results showed accurate classification, fast learning and execution in detection and classification of PQ events. Results are compared with other methods.