Application of fractional Fourier transform for classification of power quality disturbances. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Singh, Utkarsh; Singh, Shyam Narain
• Is Part Of: IET science, measurement & technology. Volume 11:Issue 1(2017)
• Page Start: 67
• Page End: 76
• Publication Date: 2017-01-01
• Subjects: Fourier transforms -- power supply quality -- feature extraction -- signal classification -- decision trees -- IEEE standards; fractional Fourier transform -- power quality disturbances -- classification technique -- multidomain feature extraction -- IEEE‐1159 standards -- decision trees -- bagging predictors; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2016.0194
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : Proper mitigation of power quality disturbances (PQDs) requires a fast, accurate and highly noise immune classification technique. This study, therefore, presents fractional Fourier transform (FRFT) based feature extraction as a new technique for classification of PQDs. FRFT is a generalised version of Fourier transform (FT) with an additional order control and can give time, frequency and intermediate time‐frequency representations for a signal. The order control offers multi‐domain feature extraction, such that most robust feature matrix is utilised for classification under any condition. An expression is derived for the optimal classification order corresponding to maximum overall accuracy. Based on IEEE‐1159 standards, 15 PQDs are simulated and a database of pure and noisy signals is prepared. Features extracted from FRFT processed signals are tested with decision trees (DTs) and bagging predictors (BPs). The proposed technique shows better performance in most of the cases, when compared with Stockwell transform based classification under similar conditions. The classification accuracies of FRFT‐DT and FRFT‐BP are impressive even with significant reduction in training and features. Further, a validation using real PQDs obtained from an experimental setup is shown. The corresponding results closely resemble the simulation outcomes.