Abstract




 
   

IJE TRANSACTIONS C: Aspects Vol. 27, No. 6 (June 2014) 881-888   

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  DISCRIMINATION OF POWER QUALITY DISTORTED SIGNALS BASED ON TIME-FREQUENCY ANALYSIS AND PROBABILISTIC NEURAL NETWORK
 
M. Hajian, A. Akbari Foroud and A. A. Abdoos
 
( Received: July 15, 2013 – Accepted: December 12, 2013 )
 
 

Abstract    Recognition and classification of Power Quality Distorted Signals (PQDSs) in power systems is an essential duty. One of the noteworthy issues in Power Quality Analysis (PQA) is identification of distorted signals using an efficient scheme. This paper recommends a Time–Frequency Analysis (TFA), for extracting features, so-called "hybrid approach", using incorporation of Multi Resolution Analysis (MRA) and Generalized S Transform (GST). Moreover, the proposed scheme is noticed to quality of features and ranking them in order to find the best combination with lower dimension. A new efficient feature ranking method namely Orthogonal Forward Selection (OFS) is applied for selection of the best subset features. Probabilistic Neural Network (PNN) as classifier is considered. An extensive series of simple and complex PQDSs are simulated to verify of suggested detection scheme. Also, sensitivity of the proposed method under different conditions of noise has been investigated. The obtained outcomes are compared with those obtained using other methods in previous research to assess the performance.

 

Keywords    Power Quality, Time–Frequency Analysis, Orthogonal Forward Selection, Multi Resolution Analysis, Generalized S Transform

 

چکیده    گسترش استفاده از تجهيزات حساس، اهميت كيفيت توان را افزايش داده است. بنابراين تشخیص و دسته بندی اغتشاشات کیفیت توان یکی از ضروريات در سیستم‌های قدرت امروزی است. یکی از مسائل ارزشمند در آنالیز کیفیت توان، تشخیص اغتشاشات با روشی موثر می‌باشد. در این مقاله، روشی موثر و ترکیبی برای استخراج ویژگی بر اساس ترکیب تبدیل S تعمیمی و تحلیل چند طیفی ارائه شده است. همچنین، روش پیشنهادی به کیفیت ویژگی‌های انتخابی و انتخاب بهترین ترکیب از آنها با کمترین ابعاد، توجه ویژه نموده است. در این مقاله، روشی جديد، بنام OFS براي انتخاب ويژگي، بکارگرفته شده و همچنین از ساختار طبقه‌بندی کننده مشهور شبکه عصبی احتمالاتی استفاده شده است. طيف وسيعي از اغتشاشات برای توجیه پذیری سیستم پیشنهادی شبیه سازی شده است. حساسیت روش پیشنهادی تحت شرایط مختلف نویزی با سطوح مختلف سیگنال همراه با نویز بررسی شده است. همچنین با مقایسه نتایج این مقاله با نتایج مقالات دیگر، کارامدی روش پیشنهادی مورد بررسی قرار گرفته است.

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