تشخیص و کلاسه بندی خطا در شبکه های هوشمند با استفاده از فازورهای ولتاژ و جریان

نوع مقاله: مقاله پژوهشی - کاربردی

نویسندگان

1 گروه مهندسی برق، موسسه اموزش عالی آیندگان، تنکابن، ایران

2 گروه مهندسی برق، دانشگاه آزاد اسلامی واحد سیاهکل، سیاهکل، ایران.

چکیده

این مقاله، یک متدولوژی جهت تشخیص و کلاسه‌بندی خطاهای رخ‌داده بر روی خطوط انتقال شبکه‌های قدرت هوشمند ارائه می‌کند. در روش پیشنهادی، فازورهای ولتاژ و جریان توسط واحد اندازه‌گیری فازور (PMU) نصب‌شده در باس ژنراتور، تخمین زده می‌شود و سپس زوایای ولتاژ و جریان معادل به دست می‌آید. این زوایا از طریق تبدیل فوریه سریع) FFT  (آنالیز می‌شوند و برای تشخیص و کلاسه‌بندی خطای خط انتقال بکار می‌روند. تشخیص خطای خط انتقال با استفاده از روش سیستم استنتاج فازی-عصبی صورت می‌گیرد و کلاسه‌بندی خطای تشخیص داده‌شده با استفاده از ماشین بردار پشتیبانی (SVM) انجام می‌شود. اعتبار سنجی روش پیشنهادی بر روی سیستم 14 باسه IEEE در محیط نرم‌افزار متلب مورد آزمایش قرارگرفته است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Detect and categorize errors in smart grids using voltage and current phases

نویسندگان [English]

  • Mohammad Saberi 1
  • Behzad Taghipoor 2
1 Department of Electrical Engineering, Ayandegan Institute of Higher Education, Iran
2 Department of electrical, Siahkal branch, Islamic Azad University, Siahkal, Iran
چکیده [English]

This paper presents a methodology for detecting and classifying the errors occurring on smart power transmission lines. In the proposed method, the voltage and current phases are estimated by the phasor measurement unit (PMU) installed in the generator bus, and then the equivalent voltage and current angles are obtained. These angles are analyzed by fast fourier transform (FFT) and used to detect of transmission line errors. Detection of the transmission line error is performed using the nerve- fuzzy inference system methodology, and the diagnostic error classification is performed using support vector machine (SVM). Validation of the proposed method for the IEEE 14 system is  also tested in the MATLAB software environment.

کلیدواژه‌ها [English]

  • Error detection and classification
  • Intelligent network
  • Phasor measurement unit
  • Fourier transform analysis
  • Nerve- fuzzy inference system
  • Support Vector Machine

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