Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2012Scopus SJRQ3The Feature Selection based Power Quality Event Classification using Wavelet Transform and Logistic Model Tree
Eristi, Huseyin; Demir, Yakup
This paper presents a new power quality event classification technique using wavelet transform and logistic model tree. The proposed method uses the samples of three cycle duration of three line voltage of power quality events. The features of these samples are obtained by using the wavelet transform and a few different feature extraction techniques. The sequential forward selection method based a feature selection process is done to ensure good classification accuracy by selecting 20 better features from all 90 features generated from the wavelet transform coefficients. The obtained features are used to train a single logistic model tree. The feasibility of the proposed algorithm has been tested using real life power quality events. The result indicates that the feature selection based proposed method reliably classifies all types of power quality events with high accuracy.
AtıfWoS7
İncele - MakaleAçık Erişim2010Scopus SJRQ1Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines
Eristi, Hueseyin; Uçar, Ayseguel; Demir, Yakup
This paper presents a new approach for the classification of the power system disturbances using support vector machines (SVMs). The proposed approach is carried out at three serial stages. Firstly, the features to be form the SVM classifier are obtained by using the wavelet transform and a few different feature extraction techniques. Secondly, the features exposing the best classification accuracy of these features are selected by a feature selection technique called as sequential forward selection. Thirdly, the best appropriate input vector for SVM classifier is rummaged. The input vector is started with the first best feature and incrementally added the chosen features. After the addition of each feature, the performance of the SVM is evaluated. The kernel and penalty parameters of the SVM are determined by cross-validation. The parameter set that gives the smallest misclassification error is retained. Finally, both the noisy and noiseless signals are applied to the classifier given above stages. Experimental results indicate that the proposed classifier is robust and has more high classification accuracy with regard to the other approaches in the literature for this problem. (C) 2010 Elsevier B.V. All rights reserved.
AtıfWoS119OpenAlex163İncele - MakaleAçık Erişim2010Scopus SJRQ1A new algorithm for automatic classification of power quality events based on wavelet transform and SVM
Eristi, Hueseyin; Demir, Yakup
This paper presents a new approach for automatic classification of power quality events, which is based on the wavelet transform and support vector machines. In the proposed approach, an effective single feature vector representing three phase event signals is extracted after signals are applied normalization and segmentation process. The kernel and penalty parameters of the support vector machine (SVM) are determined by cross-validation. The parameter set that gives the smallest misclassification error is retained. ATP/EMTP model for six types of power system events, namely phase-to-ground fault, phase-to-phase fault, three-phase fault, load switching, capacitor switching and transformer energizing, are constructed. Both the noisy and noiseless event signals are applied to the proposed algorithm. Obtained results indicate that the proposed automatic event classification algorithm is robust and has ability to distinguish different power quality event classes easily. (C) 2009 Elsevier Ltd. All rights reserved.
AtıfWoS78OpenAlex106İncele