Automatic classification of harmonic data using kkk-means and least square support vector machine

HÜSEYİN ERİŞTİ; VEDAT TÜMEN; ÖZAL YILDIRIM; BELKIS ERİŞTİ; YAKUP DEMİR

Article Details

Automatic classification of harmonic data using kkk-means and least square support vector machine

Authors:

HÜSEYİN ERİŞTİ | VEDAT TÜMEN | ÖZAL YILDIRIM | BELKIS ERİŞTİ | YAKUP DEMİR

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2015 Vol.23 Issue 5, pp.1312-1325

Document Type:

Article

Publication Date:

8/2015

Document Language:

Turkish

Document ID (Org No.):

2000742472

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

Abstract:

 

In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using kkk-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a kkk-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained through the kkk-means algorithm. To increase the efficiency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid search method and the training phase has been realized. Backpropagation neural network and J48 decision tree classifiers have been applied to the same data and results have been obtained for the purpose of comparing the performance of the LS-SVM model. The real data obtained from the output of distribution system have been used to assess the performance of the proposed classifier system. The obtained results and comparisons suggest that the proposed classifier system approach is quite efficient at classifying harmonic data.

 

In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using kkk-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a kkk-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained through the kkk-means algorithm. To increase the efficiency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid search method and the training phase has been realized. Backpropagation neural network and J48 decision tree classifiers have been applied to the same data and results have been obtained for the purpose of comparing the performance of the LS-SVM model. The real data obtained from the output of distribution system have been used to assess the performance of the proposed classifier system. The obtained results and comparisons suggest that the proposed classifier system approach is quite efficient at classifying harmonic data.

Article Details

Automatic classification of harmonic data using kkk-means and least square support vector machine

Authors: HÜSEYİN ERİŞTİ | VEDAT TÜMEN | ÖZAL YILDIRIM | BELKIS ERİŞTİ | YAKUP DEMİR

Publisher: TUBITAK

Journal: Turkish Journal of Electrical Engineering and Computer Science 2015 Vol.23 Issue 5, pp.1312-1325

Document Type: Article

Publication Date: 8/2015

Document Language: Turkish

Document ID (Org No.): 2000742472

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

HÜSEYİN ERİŞTİ | VEDAT TÜMEN | ÖZAL YILDIRIM | BELKIS ERİŞTİ | YAKUP DEMİR

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2015 Vol.23 Issue 5, pp.1312-1325

Document Type:

Article

Publication Date:

8/2015

Document Language:

Turkish

Document ID (Org No.):

2000742472

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632