Classification of Power Quality Disturbances by Using Ensemble Technique
Seckin Karasu, Saim Baskan, None None
proceeding (Uluslararası, Hakemli) | Yazar(lar): Seckin Karasu, Saim Baskan, None None | Yıl: 2016 | Dergi/Kurum: 2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU) | İndeks: WOS.ISTP | Anahtar Kelimeler: Power quality, wavelet transform, singal processing, pattern recognition, ensemble classifi
In this paper, 11 different power quality disturbances were automatically detected by using statistical features with wavelet transform and norm entropy techniques. The best of the created features were selected with forward selection algorithm. Performance of classification algorithms, Support Vector Machines (SVM), Multi Layer Perceptron (MLP), k Nearest Neighbor (KNN) and random subspace KNN (Sub-KNN) which is an ensemble method, were examined. Consequently, the best classification accuracy of 99.3% was achieved by using Sub-KNN and it was appeared that compared to other methods, this algorithm was more robust against the noise.