شبیه سازی و طبقه بندی وقایع کیفیت توان با استفاده از شبکه عصبی

نویسندگان

دانشگاه سمنان

چکیده

امروزه استفاده ی روز افزون از تجهیزات الکترونیکی و بارهای غیر خطی در سیستم قدرت، مسئله کیفیت توان را به یک موضوع مهم تبدیل کرده است. در این مقاله برای شبیه سازی وقایع کیفیت توان به طور همزمان از دو روش مدل سازی ریاضی و داده های حاصل از شبیه سازی با نرم افزار Pscad استفاده شده است. با توجه به عملکرد بسیار خوب شبکه های عصبی در کارهای تشخیص الگو و طبقه بندی، شبکه عصبی چند لایه برای طبقه بندی وقایع مورد استفاده قرار گرفته است .در مرحله استخراج ویژگی ها از دو تبدیل STFT و تبدیل موجک گسسته استفاده شده است. پس از طبقه بندی وقایع با استفاده از شبکه عصبی، مقاومت شبکه عصبی در برابر نویز در سطوح مختلف مورد بررسی قرار گرفته است. در شرایطی که نویز وجود ندارد شبکه عصبی وقایع را با دقت 98.22 درصد طبقه بندی می کند. در پایان نتایج به دست آمده در این مقاله با نتایج تحقیقات دیگر مورد مقایسه قرار گرفته است

کلیدواژه‌ها


عنوان مقاله [English]

Simulation and classification of power quality disturbances Using Neural Network

چکیده [English]

Nowadays, increasing use of electronic instruments and nonlinear loads in Power systems, make the power quality problem as one of the most important issues. In this article, the produced data from mathematical equations and PSCAD software simultaneously have been used to simulate power quality disturbances. Because of super performance of neural networks in pattern recognition and classification, the MLP neural network for classification of power quality disturbances is used in this paper. The neural networks have been developed by simulation of nonlinear terms, and they indicated their priority for pattern recognition and classification. STFT and DWT transform to extract signal's features have been used. After classification of disturbances using MLP, the neural network robustness has been examined in different levels in presence of the noise. With presence of noise, neural network classifies all the events with 98.22 percent of accuracy. Finally, results of this article are compared with other researcher's works.

کلیدواژه‌ها [English]

  • Power Quality
  • MLP Neural Network
  • Discrete Wavelet Transform
  • STFT
  • Noise

 

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