Discrimination of Power Quality Distorted Signals Based on Time-frequency Analysis and Probabilistic Neural Network

سال انتشار: 1393
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 897

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شناسه ملی سند علمی:

JR_IJE-27-6_006

تاریخ نمایه سازی: 17 خرداد 1393

چکیده مقاله:

Due to extensive utilization of sensitive devices, power quality issue has become more important than before. So, accurate recognition and classification of Power Quality DistortedSignals (PQDSs) is an essential task in the power systems. In this paper two well-known timefrequencyanalyzers i.e. Multi Resolution Analysis (MRA) and Generalized S-Transfrm (GST)are applied simultaneously for extracting of some potential features. In order to choose thebest subset features, Orthogonal Forward Selection (OFS) is used. OFS can rank features based on their severability. Probabilistic Neural Network (PNN) is considered as a powerful classifier core for discrimination of dominant selected features. Extensive samples of PQDSs aresimulated to evaluate the performance of the suggested detection scheme. Also, sensitivity of the proposed method has been investigated under different noisy conditions. At last the obtained results are compared with the accuracies of some reported methods of previous researches

نویسندگان

m Hajian

Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran

a Akbari Foroud

Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran

a.a Abdoos

Babol Noshirvani University of Technology, Babol, Mazandaran, Iran