روشی موثر در تعیین نوع خطا در خطوط انتقال با استفاده از طبقه‌بندی کنندۀ بیز مبتنی بر کرنل

نوع مقاله: پژوهشی

نویسنده

دانشگاه دامغان

چکیده

در این مقاله با استفاده از روش شناسایی الگو انواع مختلف خطا طبقه‌بندی می‌گردد. بدین منظور در ابتدا بردار ویژگی‌ها بر اساس مولفه‌های توالیِ بدست آمده از سیگنال‌های جریان و/یا ولتاژ با روش کارآمد و موثری نرمال‌سازی می‌شوند. سپس، تابع نظارتی پیشنهادی، روشِ طبقه‌بندی کنندۀ بیز مبتنی بر کرنل را بکار می‌گیرد. طبقه‌بندی کنندۀ مورد استفاده تنها با انتخاب پهنای باند تابع کرنل برای فضایِ ویژگی غیرخطی و پیچیده مناسب است. پردازش سیگنال با حداقل فرکانس نمونه‌برداری انجام می‌شود لذا از خروجی ترانسفورماتورهای جریان و ولتاژ رایج می‌توان استفاده نمود. علاوه براین، کارآمدی روش شناسایی الگو پیشنهادی از دیدگاه‌های مختلفی بررسی شده است و نتایج نشان می‌دهد حتی در شرایط نویزی، روش عملکرد قابل قبولی دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Efficient Method for Fault Classification in Transmission Line Using Kernel Naive Bayes Classifier

نویسنده [English]

  • Mohammad Pazoki
چکیده [English]

In this paper, using pattern recognition method all fault type is classified. Firstly, feature vectors obtained from sequence components of current and/or voltage signals are normalized by efficient technique. Afterwards, the proposed supervising function applies Kernel Naive Bayes classifier. The classification method through tuning of kernel function bandwidth s suitable for a complex and non-linear feature spaces. The signal processing procedures is done by using minimum sampling frequency hence the output of conventional current and voltage transformers can be utilized. Moreover, the performance of proposed pattern recognition methodology is evaluated from different point of views. The achieved results indicate that the proposed fault classifier has acceptable performance even in the noisy conditions.

کلیدواژه‌ها [English]

  • Transmission line
  • Fault classification
  • Pattern recognition
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