Outline

  • Abstract
  • Graphical Abstract
  • Keywords
  • Introduction
  • Dwt and Multi-Resolution Signal Decomposition (msd) Analysis
  • Msd Analysis
  • Modeling of Dwt and Msd
  • Application of Dwt Algorithm for Pq Disturbance Detection
  • Power Quality Signal Disturbance Detection
  • Feature Extraction Using Dwt for Classification of Pq Disturbances
  • Application of Parseval’s Theorem in Dwt for Pq Classification
  • Magnitude of an Average Absolute Sum of Detailed Coefficients
  • Classification of Pq Disturbances Based on Feature Extraction Without Noise
  • Performance of Dwt Based Mra Under Noisy Environment
  • Detection and Classification Under Noisy Environment
  • Rule Based System for an Automatic Classification of Pq Disturbances
  • Performance Comparison of Proposed Method
  • Conclusions
  • References

رئوس مطالب

  • چکیده
  • مقدمه
  • DWT و تحلیل با تجزیه‌ سیگنال چند-‌توان تفکیک
  • تحلیل MSD
  • مدل‌سازی DWT و MSD
  • کاربرد الگوریتم DWT برای آشکارسازی اختلال PQ
  • آشکارسازی اختلال سیگنال کیفیت توان
  • استخراج ویژگی با استفاده از DWT برای طبقه‌بندی اختلالات PQ
  • کاربرد قضیه‌ پارسوال در DWT برای طبقه‌بندی PQ
  • دامنه جمع متوسط مطلق ضرایب تفصیلی
  • طبقه بندی اختلال های PQ بر اساس استخراج ویژگی بدون نویز
  • عملکرد MRA مبتنی بر DWT تحت محیط نویزی
  • آشکارسازی و طبقه‌بندی تحت محیط نویزی
  • سیستم قاعد ‌محور برای طبقه‌بندی خودکار اختلالات PQ
  • مقایسه‌ عملکرد روش پیشنهادی
  • نتیجه‌گیری‌ها

Abstract

The signals in the electrical power system always have some power quality disturbances and noise contents which is the biggest obstacle in detection and time localization. In this paper, an integrated rule based approach of discrete wavelet transform – fast Fourier transform is proposed. For the detection of power quality disturbance present in the input signal, the input waveform is processed by discrete wavelet transform. The discrete wavelet coefficients are used to calculate average energy entropy of squared detailed coefficients feature. The various power quality disturbances are initially detected and then classified into four main categories as disturbances related to sag, swell, interruption and harmonics using this feature. Further classification of each main category is done using fast Fourier transform features. The total twelve types of power quality disturbances including seven basic and five combinations which are very close to real situations, are considered for the classification which are generated by parametric equations. Also, for another cases are considered by adding noise to four basic disturbances sag, swell, harmonics and flicker. All sixteen cases are simulated using Mathworks Matlab R2008b. The performance of classifier is tested for 150 test signals for various durations with different disturbances with and without noise. The developed classifier is able to achieve 99.043% accuracy. From the simulation results, it can be seen that the proposed approach is effective for the detection and classification of various power quality disturbances.

Keywords: - - - -
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