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GÜÇ KALİTESİNDEKİ BOZULMA TÜRLERİNİN SINIFLANDIRILMASI İÇİN BİR ÖRÜNTÜ TANIMA YAKLAŞIMI

A PATTERN RECOGNITION APPROACH FOR CLASSIFICATION OF POWER QUALITY DISTURBANCE TYPES

Journal Name:

  • Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

Publication Year:

  • 2011

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
In this study, an algorithm based on pattern recognition approach is proposed for classification of power quality disturbance types. For feature extraction which is an important part of the pattern recognition, a method based on entropy which uses the decomposition coefficients of wavelet transform is presented. The most important advantage of the method is the reduction of data size without losing main distinguishing characteristics of signal. Support vector machines based on statistical learning theory is used as a classifier. The performance of the proposed algorithm is evaluated by using real and synthetic power quality disturbance data. Real power quality disturbance data are obtained from our national power system. Besides, the synthetic power quality disturbance data are obtained from ATP/EMTP and mathematical models. The analyses and results obtained in this study show that proposed algorithm has an efficient, feasible and practical structure.
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Abstract (Original Language): 
Bu çalışmada, güç kalitesindeki bozulma türlerinin sınıflandırılması için örüntü tanıma yaklaşımlarına dayalı bir algoritma önerilmiştir. Örüntü tanımanın önemli bir kısmı olan özellik çıkarma için, dalgacık dönüşümünün ayrıştırma katsayılarını kullanan entropi temelli bir yöntem sunulmuştur. Yöntemin en önemli avantajı, işaretin ayırt edici özelliklerini kaybetmeksizin veri boyutunu indirgeyebilmesidir. Sınıflandırıcı olarak, istatistiksel öğrenme teoremine dayanan destek vektör makineler kullanılmıştır. Önerilen algoritmanın başarımı, gerçek ve yapay güç kalitesi bozulma verileri kullanılarak değerlendirilmiştir. Gerçek güç kalitesi bozulma verileri, ulusal enerji sistemimizden elde edilmiştir. Yapay veriler ise, ATP/ EMTP modelinden ve matematiksel modellerden elde edilmiştir. Çalışmadan elde edilen analiz ve sonuçlar, önerilen algoritmanın etkin, güvenilir ve uygulanabilir bir yapıya sahip olduğunu göstermektedir.
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