Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2020Scopus SJRQ1/r/munzur/3232
  Approximate-derivative-based signalprocessing method to segment power-quality disturbances

  Akmaz, Düzgün

  The increasing complexity of power systems and the increase in power quality (PQ) data have made it necessary to develop different and simple signal-processing tools. In this study, an approximate-derivative (AD) signal-processing tool based on a simple mathematical processing approach was developed for the segmentation of PQ disturbances. Although the developed method was highly effective for the segmentation of noise-free signals, the method was unable to properly handle the segmentation of noisy signals. Thus, to mitigate this situation, a denoising method based on the Sqtwolog threshold was applied to the noisy signals. After denoising, the proposed AD method effectively performed the segmentation. Subsequently, AD and a single-level discrete wavelet transform (DWT) with Daubechies 4 mother wavelets were compared through simulations, which showed that successful results can be obtained using the proposed method. Furthermore, all simulations showed that the application of AD and single-level DWT to PQ signals under different conditions resulted in similar patterns with different amplitudes. Therefore, this study provides a different approach for analysing signal using single-level DWT.

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  AtıfScopus5·WoS4·OpenAlex5

  İncele
• MakaleAçık Erişim2012Scopus SJRQ1/r/munzur/4032
  Automatic classification of power quality events and disturbances using wavelet transform and support vector machines

  Eristi, H.; Demir, Y.

  In this study, a new approach for the classification of power quality events is presented. Also, power quality disturbances, which occur in each phase of the power system after a fault event, are classified with the proposed system. In the proposed recognition system, three-phase voltage signals are used in order to identify the type of power quality events. Three-phase voltage signals are subjected to normalisation and segmentation processes. A wavelet transform method is used in order to obtain the distinctive features of event signals. An efficient feature vector, which represents the distinctive characteristics of three-phase event voltage signals and reduces data size, is extracted by applying the two-stage feature extraction process. Power quality event types are determined by using a support vector machine classifier. At the last stage of intelligent recognition system, types of power quality disturbances regarding each fault event are identified by doing a further analysis. Real power system data are used to evaluate the performance of the proposed approach. According to the obtained results, proposed intelligent recognition system classifies power quality event types with a high accuracy. The analyses and results also show that the proposed approach is efficient, reliable and applicable.

  AtıfWoS68·OpenAlex101

  İncele