Abstract
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.
-
Kapsamı
Uluslararası
-
Type
Hakemli
-
Index info
WOS.ISTP
-
Language
Turkish
-
Article Type
None
-
Keywords
Power quality wavelet transform singal processing pattern recognition ensemble classification