Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2014Scopus SJRQ1/r/munzur/4108
  Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine

  Eristi, Huseyin; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup

  In this paper, a new S-Transform and Extreme Learning Machine (ST-ELM)-based event recognition approach for the purpose of classifying power quality (PQ) event signals automatically has been proposed. In this approach, the distinctive features of the PQ event signals have been obtained with the S-Transform-based feature extraction. The feature vector obtained with feature extraction has been applied as input to the ELM classifier. Ten different classification procedures were determined within the framework of this study to assess the performance of the ELM classifier on PQ event data. Real PQ event data and synthetic PQ event data obtained from MATIAB/Simulink environment have been used in these procedures. Also, three different PQ event data sets, which are formed by adding noises of 20, 30 and 50 dB to the synthetic PQ event data respectively, have been used in order to assess the performance of the proposed approach on noisy conditions. According to the results of performance evaluations, the proposed ST-ELM-based PQ event recognition system has a very high performance of recognizing PQ event data. Besides, classification of noisy data showed that the proposed approach is robust at recognizing noisy data. The performance of the ST-ELM-based recognition system on PQ data shows that this approach has an effective recognition structure that can be used in real power systems. (C) 2014 Elsevier Ltd. All rights reserved.

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  AtıfWoS63·OpenAlex93

  İncele
• MakaleAçık Erişim2013Scopus SJRQ1/r/munzur/4149
  Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines

  Eristi, Huseyin; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup

  In this paper, a new optimal feature selection based power quality event recognition system is proposed for the classification of power quality events. While Apriori algorithm is capable of processing categorical data, an effective feature vector, which represents distinctive features of digital power quality event data, has been obtained by means of the proposed k-means based Apriori algorithm feature selection approach. The proposed k-means based Apriori algorithm feature selection approach is presented with a power quality event recognition system. In the power quality event recognition system, normalization and segmentation processes have been applied to three-phase event voltage signals. Using 9-level multiresolution analysis, wavelet transform coefficients of the event signals have been obtained. By applying nine different feature extraction processes to these coefficients, a 90 dimensional feature vector belonging to three-phase event voltage signals has been extracted. Optimal feature vector has been obtained by applying the k-means based Apriori algorithm feature selection approach to the obtained feature vector, which has been applied as the last step to the input of the least squares support vector machine classifier and recognition performance results have been obtained. Real power quality event data have been used to evaluate the performance of the proposed feature selection approach and power quality event recognition system. According to the results, the proposed k-means based Apriori algorithm feature selection approach and power quality event recognition system are efficient, reliable and applicable and classify three-phase event types with a high degree of accuracy. (C) 2013 Elsevier Ltd. All rights reserved.

  AtıfWoS98·OpenAlex131

  İncele